ISBIS 2019 Satellite Conference August 15-16 2019 Lanai Kijang … book ISBIS 2019...
Transcript of ISBIS 2019 Satellite Conference August 15-16 2019 Lanai Kijang … book ISBIS 2019...
ISBIS
2019 Satellite Conference
August 15-16 2019
Lanai Kijang
Kuala Lumpur
Malaysia
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We thank our partners:
• Bank Negara Malaysia
• Department of Applied Statistics, Faculty of Eco-
nomics and Administration, University of Malaya
• Malaysian Economic Association.
We thank our sponsors:
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Scientific Programming Committee:
• Julie Novak, Netflix, USA
• Fugee Tsung, University of Science & Technology, Hong Kong
• Bovas Abraham, University of Waterloo, Canada
• Wei Liem Loh, National University of Singapore
• Refik Soyer, George Washington University, USA
• Asha Gopalakrishnan, Cochin University of Science and Technology, India
• Wee-Yeap Lau, University of Malaya, Malaysia
• Song Won Park, University of Sao Paulo, Brazil
• Toh Hock-Chai, Bank Negara Malaysia (co-chair)
• Martina Vandebroek, KU Leuven, Belgium (chair)
Local Organising Committee:
• Lau Wee Yeap (chair)
• Muzalwana Abdul Talib
• Yee Chee May
• Ahmad Farid Osman
• Soon Siew Voon
• Hanija Jaafar
• Wan Rosman Effendi
• Ng Sor Tho
• Tey Nai Peng
• Ng Yin Mei
• Adilah Abdul Ghapor
• Lai Siow Li
• Diana Abdul Wahab
• Martina Vandebroek 3
PROGRAM
THURSDAY AUGUST 15 2019
8:30 - 9:00 Registration
9:00 - 9:15 Welcome
9:15 - 10:25 Keynote: Galit Shmueli (Multi-purpose Hall)
To Explain, To Predict, or To Describe?
Chair: Martina Vandebroek
10:30 - 10:50 Coffee/Tea break
10:50 - 12:20 STATISTICS IN FINANCE
Chair: Indranarain Ramlall (Meeting Room 1)
Indranarain RamlallA Global Analysis of Public Debt-Economic Performance Nexus in light of
the US Subprime Crisis
Kee-En Lim and Wee-Yeap Lau
Effects of 2018-19 US-China Trade War on USD/RMB Exchange Rate Vo-
latility
Ahmad Maulin Naufa
The Impact of Foreign Ownership to Return Volatility, Volume and Risk of
Stock: Evidence from ASEAN Countries
10:50 - 12:20 STATISTICAL LEARNING APPLICATIONS
Chair: Ekin Tahir (Meeting Room 2)
Luca Frigau
Regularization in tree-based classification models
Vadim Sokolov
Strategic Bayesian Asset Allocations
Tahir Ekin
Probabilistic Variable Accuracy Algorithm
12:20- 13:30 Lunch
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13:30 - 15:00 NOVEL TIME SERIES APPROACHES WITH BUSINESS AND
INDUSTRY APPLICATIONS
Chair: Nalini Ravishanker (Meeting Room 1)
Katherine B. Ensor
Time-varying wavelet-based applications for evaluating the Water- Energy
Nexus
Balaji Raman
Return on Investment Calculator for Promotions in CPG Industry
Suparna Biswas
Non-parametric Estimation of Spectral Risk Measures
13:30 - 15:00 BAYESIAN METHODS FOR BUSINESS AND INDUSTRIAL
STATISTICS
Chair: Refik Soyer (Meeting Room 2)
Hedibert Lopes
Analysis of time series of proportions: a linear Bayes approach
Refik Soyer
A Bayesian Competing Risks Model for Assessment of Mortgage De-
fault/Prepayment
Thomas Mazzuchi
Reliability Growth Test and Evaluation for Multistage Systems
13:30 - 15:00 CONTRIBUTED SESSION 1
Chair: Pooi Ah Hin (Discussion Lounge)
Pooi Ah Hin
Asset Pricing Model with Jumps
Ng Yew Seong
A Portfolio Trading Strategy
Wayan Nuka Lantara
The impact of Net Stable Funding Ratio: Evidence from Indonesian Banks
Soon Siew Voon
Asymmetry Causal Relationship between Exchange Rate and Interest Rate
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15:00 - 15:20 Coffee/Tea break
15:20 - 16:50 TIME SERIES IN FINANCE
Chair: Katherine B. Ensor (Meeting Room 1)
Ananya Lahiri
A model driven by mixed fractional Brownian motion in context of finance
Anindya Goswami
Testing of Binary Regime Switching Models using Squeeze Duration Analysis
Nalini Ravishanker
Clustering High-Frequency Financial Time Series Based on Mutual Infor-
mation
15:20 - 16:50 MACHINE LEARNING IN BUSINESS
Chair: David Banks (Meeting Room 2)
David Banks
The Bright Future of Experimental Design
Aniruddha Pant
Applications of Machine Learning in Healthcare
15:20 - 16:50 MEASURING PRICES OF REAL ESTATE AND ITS
CHALLENGES
Chair: Toh Hock Chai (Discussion Lounge)
Jens Mehrhoff
Commercial Real Estate Indicators: Prices and Beyond
Nur Fazila Mat Salleh
Enterprise Data Governance and Direction Going Forward
Soong Kim Loong
Data Governance: Adapting to the Digital Age
Silke Stapel-Weber
ECB data governance for better analysis and decision-making
16:20 - 16:30 Comfort break
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16:30 -18:00 APPLIED STATISTICS
Chair: Muzalwana Binti Abdul Talib (Meeting Room 1)
Muzalwana Binti Abdul Talib
Air Quality Monitoring with Exponentially Weighted Moving Average
(EWMA) control charts
Ahmad Farid Osman
Forecasting weekly electricity demand in Malaysia using exponential
smoothing approaches
Siow-Li Lai
The non-linear nexus between financial development and fertility: A dyna-
mic panel GMM analysis
16:30 -18:00 RECENT DEVELOPMENTS IN COMPLEX DEPENDENT
DATA
Chair: Lionel Truquet (Meeting Room 2)
Liudas Giraitis
Standard testing procedures for white noise and heteroscedasticity
Lionel Truquet
Time-varying time series models for categorical data
16:30 -18:00 CONTRIBUTED SESSION 2
Chair: Wiji Tri Wilujeng (Discussion Lounge)
Wiji Tri Wilujeng
Advertised Landed House Price in Balikpapan
Deja Firda
Bivariate Landed House Data using Minimum Area of Ellipse
Astari Raihanah
Pearson product moment is used more than just measuring correlation
Ang Siew Ling
Improvement of Reserve Estimate Using Information on Subclasses of In-
surance
18 00 - 20:00 WELCOME BANQUET (Residential Cafe)
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FRIDAY AUGUST 16 2019
8:30 - 9:00 Registration
9:00 - 10:30 ADVANCED METHODOLOGICAL CONTRIBUTIONS IN
TIME SERIES
Chair: Yuanbo Li (Meeting Room 1)
Yuanbo LiGroup Orthogonal Greedy Algorithm for Change-point Estimation of Multi-
variate Time Series
Zhan LiuFitting time series models for longitudinal surveys with non-ignorable mis-
sing data
Chi Tim NgReal time prediction of irregular periodic time series data
9:00 - 10:30 COMPUTER EXPERIMENTSS
Chair: Alessandro Fasso (Meeting Room 2)
Matthias HwaiGaussian Process Modeling and Optimization of Simulators for Physical
Systems
Xinwei DengGaussian Process Models for Computer Experiments with Quantitative and
Qualitative Inputs
Alessandro FassoGaussian Processes and LASSO for change detection of 4D climate datasets
9:00 - 10:30 CONTRIBUTED SESSION 3
Chair: Titi Kanti Lestari (Discussion Lounge)
Tigor Nirman SimanjuntakGender statistics of household fisheries industry in Indonesia
Suryo Adi RakhmawanThe gender gap on formal worker participation in Indonesia: Trend and the
way forward
Erli Wijayanti PrastiwiWomen and technical efficiency analysis of the creative manufacturing in-
dustry in Indonesia
Lilis Heri Mis CicihGender Mainstreaming in Fisheries’ Household in Indonesia: Statistical
Analysis
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10:30 - 10:50 Coffee/Tea break
10:50 - 12:20 RELIABILITY MODELING AND PROCESS MONITORING
Chair: Sheng-Tsaing Tseng (Meeting Room 1)
Yi-Fu Wang
End of Performance Prediction of Lithium-ion Batteries
Su-Fen Yang
A New Loss Control Chart for Monitoring the Deviation of the Quality
Variable from the Target Value
Sheng-Tsaing Tseng
Misspecification Analysis of a pH Acceleration Model
10:50 - 12:20 APPLICATIONS OF DEPENDENT DATA
Chair: Rahim Mahmoudvand (Meeting Room 2)
Vincent Raja
Bivariate Discrete Distributions for Load Sharing Models with Applications
Rahim Mahmoudvand
Is the Exchange Rate Predictable in Long-Run: Experiences from Iranian
Currency?
Diana Abdul Wahab
Decomposition of Graduate’s Gender Wage Gap in the Public and Private
Sectors in Malaysia.
10:50 - 12:20 CONTRIBUTED SESSION 4
Chair: Shinsuke Kamoto (Discussion Lounge)
Shinsuke Kamoto
Capacity expansion and financial leverage under a potential entry threat
Kwardiniya Andawaningtyas
Analysis of grouping ABC - VED and predicting the number of requests
Fethi Ozbek
Poultry industry production statistics in Turkey
Ani Budi Astuti
Series of Activities for Increasing the Mount Bromo Tourism Business in
Indonesia through Modeling Nonlinear Principal Component Analysis.
12:20- 13:30 Lunch
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13:30 - 15:00 Short course 1: Bayesian Data Analysis (Multi-purpose Hall)
15:00 - 15:25 Coffee/Tea break
15:25 - 16:25 Short course 2: Bayesian Data Analysis (Multi-purpose Hall)
16:25 - 16:45 Comfort break
16:45 -18:00 Short course 3: Bayesian Data Analysis (Multi-purpose Hall)
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ABSTRACTS
Keynote presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Statistics in Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
y-bis invited session: Statistical Learning Applications . . . . . . . . . . . . . . . . . . . 15
Novel Time Series Approaches with Business and Industry Applications17
ISBA invited session: Bayesian Methods for Business and IndustrialStatistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Contributed session 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
Time Series in Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Machine learning in business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Measuring prices of real estate and its challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Applied statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
IASC invited session: Recent development in complex dependent data . 32
Contributed session 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
Advanced Methodological Contributions in Time Series . . . . . . . . . . . . . . . . . . . 35
Computer Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Contributed session 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
Reliability Modeling and Process Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Applications of Dependent Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Contributed session 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47
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KEYNOTE SPEAKER:
Galit Shmueli, National Tsing Hua University, Taiwan
Title: To Explain, To Predict, or To Describe?
Abstract: Statistical modeling is a powerful tool for developing and testing theories by
way of causal explanation, prediction, and description. However, modeling for the purpose
of causal explanation, prediction, or description call for substantially different modeling
and evaluation processes. Conflation of the three as well as under-appreciation of one or
more of these components are dangerous both to scientific research and to practice. In
this talk I will clarify the distinction between the three types of modeling and describe
practical implications in terms of the data analysis process.
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ORGANIZER: WeeYeap Lau, University of Malaya, Malaysia
SESSION TITLE: Statistics in Finance
CHAIR: Indranarain Ramlall, University of Mauritius
• Indranarain Ramlall, University of Mauritius
Title: A Global Analysis of Public Debt-Economic Performance
Nexus in light of the US Subprime Crisis
Abstract: This paper focuses on the interaction between public debt and econo-
mic performance by introducing new instrumental variables, let alone an assessment
of the impact of the 2007-2008 crisis. Findings show that estimation based on over-
looking of endogeneity problem leads to inconsistent sign effects with respect to
public debt. A crisis-induced shift in the tipping point of public debt is noted from
77% to 92%. Similarly, the optimal level of GFCF underwent an increase of 15% post
the onset of the crisis to 52%. For most variables, the effects tend to be pronounced
on real GDP per capita relative to nominal GDP per capita. Continental dum-
mies show high levels of public debts prevailing in America and Asia. Policy-wise,
structural changes may be required in order to effectively deal with crisis-induced
shifts.
• Kee-En Lim and Wee-Yeap Lau, University of Malaya, Malaysia
Title: Effects of 2018-19 US-China Trade War on USD/RMB Ex-
change Rate Volatility
Abstract: This paper examines the potential impact of the trade war on Chinese
currency, RMB. Time series: GARCH model is used to measure the volatility of
RMB before and after the trade war. Initial result reveals that volatility of RMB
increases after Trump became the President. Structural change test also reveals
that the volatility begin after Trump became the president. More GARCH time
series analysis will be conducted on the data. The final implication of this paper is
to prove that: (1) US-China trade war affects RMB volatility (2) determine and me-
asure of the effects of the volatility of China-US investment and international flow.
Using the same program codes and model we will examine the currency volatility
on other currencies from other trade wars. Eventually, we wish to answer whether
the 2018/2019 US-China trade war is affecting RMB volatility.
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• Ahmad Maulin Naufa, Wayan Nuka Lantara; Wee-Yeap Lau , Uni-
versitas Gajah Mada, Malaysia
Title: The Impact of Foreign Ownership to Return Volatility, Vo-
lume and Risk of Stock: Evidence from ASEAN Countries
Abstract: This paper aims to test the effect of foreign ownership on the re-
turn volatility, trading volume, and risk of the stock. Based on a panel data from
six ASEAN Countries (Indonesia, Thailand, Malaysia, Singapore, Philippines, and
Vietnam) from 2011 to 2017. Our results show: First, foreign ownership has a nega-
tive relationship to return volatility and risk of the stock. Second, foreign ownership
has a positive effect on the trading volume. The models are further extended to in-
clude the lag variables and control variables for the currency, domestic shareholders
and others. Although the level of foreign ownership is different for each ASEAN
member countries, our results show the benefit outweighs the cost. The presence of
foreign ownership contributes to the stability of the capital market besides facilita-
ting better governance of the listed firms
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ORGANIZER: Ekin Tahir, Texas State University, USA
SESSION TITLE: y-bis Session: Statistical Learning Applications
CHAIR: Ekin Tahir, Texas State University, USA
• Luca Frigau, University of Cagliari,Italy
Title: Regularization in tree-based classification models
Abstract: In order to enhance the performance of tree-based classifiers, both in
terms of prediction error and interpretability, several strategies may be used, such as
predictor subset selection and regularization. The main problem with these methods
is that they often exhibit high variance. The greedy search for splitting variables
means that small perturbations of the data can lead to dramatically different trees.
In the same way that L1 and L0 penalties improve regression performance, we seek
similar strategies in the context of tree-based classification. In this work we propose
a new regularization strategy for tree-based classification models, one that enhances
model interpretability. It penalizes the number of different variables used in the
tree, and employs bounded greed in building the classification tree. Specifically,
at each node, the choice of the splitting variable favors variables that have already
been used in the tree
• Vadim Sokolov, George Mason University, USA
Title: Strategic Bayesian Asset Allocations
Abstract: Srategic asset allocation requires an investor to select stocks from
a given basket of assets. Bayesian regularization is shown to not only provide
stock selection but also optimal sequential portfolio weights. The perspective of the
investor is to maximize alpha risk-adjusted returns relative to a benchmark index.
Incorporating investor preferences with regularization is related to the approach
of Black (1992) and Puelz (2015). Tailored MCMC algorithms are developed to
calculate portfolio weights and perform selection. We illustrate our methodology
with an application to stock selection from the SP100, and the top fifty holdings of
Renaissance Technologies and Viking Global hedge fund portfolios.
• Ekin Tahir, Texas State University, USA
Title: Probabilistic Variable Accuracy Algorithm
Abstract: The amount of improper payments in U.S. was estimated to be
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141 billion dollars as of 2015. Data accuracy and matching are critical to prevent
such overpayments in many federal benefit programs. Existing exact data mat-
ching algorithms may not address challenges such as the lack of information about
some variables, inaccuracy of self-reported data and matching of data with different
frequencies. We propose Probabilistic Variable Accuracy (PVA) algorithm as a pro-
babilistic data matching classifier. PVA can be used in the initial application and
continuous verification phases. If the provided information is found to be unlikely
in the initial application, the applicant could be asked to confirm the information
before even that application is processed. This can help eliminate some of the basic
applicant mistakes and errors. As part of the continuous verification efforts, PVA
can flag potential life changing events. The underlying idea of PVA is to model how
likely it is to see a particular input value given all other variables and the context.
It is based on basic Bayesian updating via the use of Bayes theorem. This is more
comprehensive than only scoring the eligibility of the application, in addition it
scores the probabilistic accuracy of the reported variable inputs of the data. The
output of the PVA algorithm for each variable and the applicant can be fed into a
decision tool which would compare it with the specified thresholds.
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ORGANIZER: Nalini Ravinshaker, University of Connecticut, USA
SESSION TITLE: Novel Time Series Approaches with Business and
Industry Applications
CHAIR: Nalini Ravishanker, University of Connecticut, USA
• Katherine B. Ensor, Rice University, USA
Title: Time-varying wavelet-based applications for evaluating the
Water- Energy Nexus
Abstract: World leaders have become increasingly concerned about the water-
energy nexus, a concept that refers to the necessity of water in energy production
and the consumption of energy in the extraction, purification, and delivery of wa-
ter. In this paper, we quantify the dynamic relationship between energy and water
commodities. Commodity markets are complex with a wide variety of participants
having different objectives, resulting in nonstationary time series formed by combi-
nations of different components operating at different temporal frequencies. Using
daily water and energy commodity ETF price data from 2007 to 2017 to assess
this complex relationship. The statistical novelty of our approach lies in the use of
time-varying multivariate wavelet techniques with optimal selection of the wavelet
coefficients. Visualization of the structure in multidimensional time series illustrates
the value added in applying time-varying techniques to quantify the relationships
between nonstationary time series.
• Balaji Raman, Cogitaas, India.
Title: Return on Investment Calculator for Promotions in CPG In-
dustry
Abstract: To drive sales, CPG firms provides two types of promotions trade and
consumer promotions. Trade promotions are incentives given to retailers. Consumer
promotions are incentives to consumers to drive sales, market share and penetration.
Nature of promotions vary by SKUs (Stock Keeping Units) and channels. In this
talk, we describe a comprehensive DLM framework to measure promotion effecti-
veness at different levels of hierarchy store, cluster of stores, channels and regions.
This method also accounts for cannibalization across SKUs. The computational
framework for models is R-INLA.
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• Suparna Biswas, ISI Chennai, India
Title: Non-parametric Estimation of Spectral Risk Measures
Abstract: Spectral risk measures (SRMs) belongs to the family of coherent
risk measures. We propose a kernel based estimator of SRM. We prove that the
estimator is strongly consistent and the estimator is asymptotically normal. We
compare the finite sample performance of the kernel based estimator with that of
empirical estimator of SRM using Monte Carlo simulation. Based on our simulation
study we have estimated the exponential SRM of four future index i.e. Nikkei 225,
Dax, FTSE 100 and Hang Seng using our proposed kernel estimator. We shall also
discuss that Kaplan-Meier estimate is a preferable choice than empirical cdf when
we deal with left-truncated right-censored data in the estimation of SRM
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ORGANIZER: Refik Soyer, George Washington University, USA
SESSION TITLE: ISBA invited session: Bayesian Methods for Bu-
siness and Industrial Statistics.
CHAIR: Refik Soyer, George Washington University, USA
• Hedibert Lopes, INSPER, Sao Paulo, Brazil; Refik Soyer, George
Washington University, USA
Title: Analysis of time series of proportions: a linear Bayes approach
Abstract: Time series of proportions arise in many applications in economics,
finance and marketing. Bayesian analysis of such time series poses computational
challenges due to the lack of analytical forms for sequential Bayesian updating and
requires the use of Markov chain Monte Carlo (MCMC) methods. MCMC methods
are not computationally efficient for sequential analysis and thus, are not attractive
for real-time online processing of time series of proportions. We propose a dynamic
general linear model setup for analysis of proportions and develop linear Bayesian
inference using a class of conjugate priors. We illustrate the implementation of
our approach using actual marketing data and compare our results with previous
findings.
• Refik Soyer, George Washington University, USA
Title: A Bayesian Competing Risks Model for Assessment of Mort-
gage Default/Prepayment
Abstract: In this paper we present a Bayesian competing risks proportional ha-
zards model to describe mortgage defaults and prepayments. We develop Bayesian
inference for the model using Markov chain Monte Carlo methods. Implementation
of the model is illustrated using actual default/prepayment data and additional
insights that can be obtained from the Bayesian analysis are discussed.
• Thomas Mazzuchi, Valeriya Malobrodskaya, Shahram Sarkani, Ge-
orge Washington University, USA
Title: Reliability Growth Test and Evaluation for Multistage Sys-
tems
Abstract: The goal of developmental testing is to ensure that a certain reli-
ability threshold is achieved and to eliminate existing defects before the system is
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fielded. However, reliability prediction through testing can be a challenging process
due to budget constraints and calculation complexity. Military systems, such as
torpedoes and missiles, known as single-shot systems, introduce an additional chal-
lenge. Due to their ever-increasing complexity and growing demands for the reliable
field performance, reliability prediction continues to be one of the biggest challenges
in the development of the military systems. This research describes a methodo-
logy to overcome the aforementioned challenges and enable the measurement and
prediction of reliability growth of a multistage single-shot system. It proposes the
incorporation of existing test data with expert judgment in a Bayesian framework
that allows point and interval estimation of current and future reliability values, as
well as addressing important managerial questions regarding optimal amount of tes-
ting and number of remaining defects. The proposed framework enables continuous
updating of predicted reliability values, removing the dependence on the estimates
formed at the initial stage of testing.
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CONTRIBUTED SESSION 1
CHAIR: Pooi Ah Hin, Sunway University, Malaysia
• Pooi Ah Hin and Ng Yew Seong, Sunway University, Malaysia
Title: Asset Pricing Model with Jumps
Abstract: . The future asset price which is non-concordant with the distribution
estimated from the price today and the prices on a large number of previous days
may be referred to as a jump. The three major characteristics of a jump are the
length of the interval between the occurrence time of the previous jump and that
of the present jump, the indicator which denotes that the jump is up or down by
its values +1 and -1 respectively, and the degree of non-concordance given by the
negative logarithm of the probability of the left tail or right tail of which one of the
end points is given by the observed future price. The vector of three major charac-
teristics of the next jump is modelled to be dependent on the vector corresponding
to the present jump via a 3-dimensional conditional distribution which is derived
from a 6-dimensional power-normal mixture distribution. The distribution the fu-
ture asset price is modelled as a mixture of the distribution based on the historical
data on prices and the distribution of the jump. From the mixture distribution, we
find a nominally-95% prediction interval for the future asset price. The coverage
probability of the prediction interval is found to be much closer to the target value
of 0.95 than that of the prediction interval based on the model which ignores the
jumps
• Ng Yew Seong and Pooi Ah Hin , Sunway University, Malaysia
Title: A Portfolio Trading Strategy
Abstract: This paper aims to derive a strategy to trade a portfolio of two selected
stocks from the Malaysian share market on a monthly basis, that is with a holding
time of one month. Consider a portfolio of wi units of Stock i , i=1,2, with w1 +
w2 = 1. We use the present months values of two latent variables extracted from
a set of 17 selected Malaysian macroeconomic variables, the Malaysian composite
index and volume, and the prices and volumes of Stocks 1 and 2, as the predictors
to predict the next months portfolio value using the multivariate power-normal
distribution. In the proposed portfolio trading strategy, we invest in the portfolio
21
if the estimated probability of making a profit is larger than half, using a value of
w1 which maximizes the average gain per unit of the sum invested. The numerical
results show that compared with the situation in which the estimated probability
of making a profit is less than half , the situation in which the same probability is
larger than half would offer a better opportunity for investing in the portfolio, and
we would make a bigger profit if the optimal w1 is chosen.
• Wayan Nuka Lantara, Ahmad Maulin Naufa, Bowo Setiyono, Uni-
versitas Gadjah Mada, Indonesia
Title: The impact of Net Stable Funding Ratio: Evidence from Indo-
nesian Banks
Abstract: This research aims to explore deeply the impact of Net Stable Funding
Ratio (NSFR) in Indonesian banks. The Indonesian government has issued the rule
of minimum NSFR at least 100% since 2017. Whether the higher NSFR the better
impact to the Indonesian bank, or at the certain level the reversal effect would occur.
Therefore, is the relationship of NSFR to the bank performance in Indonesia either
linear or non-linear? This research would examine that effect on the Indonesian
Banks (private and state) during 2017-2018. We conducted the regression analysis
(ordinary least square) with some multiple robustness tests to test that relationship.
We find that the NSFR provide benefits to enhance the bank performance (net in-
come, ROA, and ROE). The year where the NSFR is mandatory, Indonesian banks
tend to adjust their NSFR to be lower than the previous years where NSFR is not
compulsory.
• Soon Siew Voon, University of Malaya, Malaysia and Ahmad Zu-
baidi Baharumshah, Universiti Putra Malaysia, Malaysia
Title: Asymmetry Causal Relationship between Exchange Rate and
Interest Rate
Abstract: This paper revisits the empirical relationship between the real ex-
change rate and real interest for 8 Asian countries. Our analyses reveal that the
nominal exchange rate is directly responsible for mean-reverting behavior in real
exchange rate in most of the Asian countries. Based on the asymmetric causality
test, we find the dynamic between nominal exchange rate and relative prices has
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changed after the Asian Financial crisis (AFC). The causal link amongst the inte-
rest rate differential and nominal exchange rate also behave asymmetric across the
sample periods.
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ORGANIZER: Katherine B. Ensor, Rice University, USA
SESSION TITLE: Time Series in Finance
CHAIR: Katherine B. Ensor, Rice University, USA
• Ananya Lahiri, IIT Tirupati, India
Title: A model driven by mixed fractional Brownian motion in con-
text of finance
Abstract: We will study a model driven by mixed fractional Brownian mo-
tion, some of its statistical properties and also see some application of this model in
context of finance. We also perform some simulation study in support of our results.
• Anindya Goswami, IISER, Pune India.
Title: Testing of Binary Regime Switching Models using Squeeze Du-
ration Analysis
Abstract: We have developed a statistical technique to test the model assump-
tion of binary regime switching extension of the geometric Brownian motion (GBM)
model by proposing a new discriminating statistics. Given a time series data, we
have identified an admissible class of the regime switching candidate models for
the statistical inference. By performing several systematic experiments, we have
successfully shown that the sampling distribution of the test statistics differs dras-
tically, if the model assumption changes from GBM to Markov modulated GBM,
or to semi-Markov modulated GBM. Furthermore, we have implemented this sta-
tistics for testing the regime switching hypothesis with Indian sectoral indices. The
analysis results in rejection of GBM and non-rejection of semi-Markov modulated
GBM hypotheses.
• Nalini Ravishanker, University of Connecticut, USA
Title: Clustering High-Frequency Financial Time Series Based on
Mutual Information
Abstract: High-frequency transaction-by-transaction financial data are readily
available to investors and researchers who seek to analyze these data in order to
understand patterns in the data that will facilitate risk management. Clustering
the stocks based on properties such as returns is often an attractive step to studying
interdependency patterns between them. Under an efficient clustering scheme, we
24
would expect “similar” stocks that stochastically move together (or commove) to be
grouped into the same cluster. The clusters can then provide insight into properties
of the stocks and investors can avoid volatility risk by allocating investments among
the stocks in different clusters. In this talk, our goal is to describe patterns of
comovement over multiple trading days by clustering the stocks using the mutual
information between them. We look at two criteria to decide the optimal number of
clusters, the elbow change point approach and the average silhouette value method.
The elbow change point method tends to generate a small number clusters with
relatively large cluster sizes within any given trading day. By contrast, the average
silhouette value method provides a larger number of smaller sized clusters within a
trading day. We then estimate the comovement probability of top m-tuples of stocks
under the two methods and discuss similarities and differences between them. This
is joint work with Haitao Liu and Jian Zou, Worcester Polytechnic Institute..
25
ORGANIZER: David Banks, Duke University & SAMSI, USA
SESSION TITLE: Machine learning in business
CHAIR: David Banks, Duke University & SAMSI, USA
• David Banks, Duke University & SAMSI
Title: The Bright Future of Experimental Design
Abstract: For decades, I thought that experimental design had become an
intellectual backwater of statistics—useful for some things, but without much new
energy. The machine learning world has breathed new life into that subdiscipline.
This talk describes some of the new challenges and new heuristics that are driving
design forward
• Aniruddha Pant, CEO of AlgoAnalytics
Title: Applications of Machine Learning in Healthcare
Abstract: I will discuss our work in image analytics as well as text analytics
in healthcare domain. Business problems that we will discuss will range from using
AI to assist digitization of healthcare data to using this digitized data to provide
diagnostic assistance to healthcare providers. In specific I will talk about a suit
of Analytics components around Electronic Healthcare data. On the technology
side we will cover application of classical machine learning techniques like Random
Forests and Support Vector Machines as well as deep learning methods like convo-
lutional neural networks and recurrent neural networks.
26
ORGANIZER: Toh Hock Chai, Bank Negara, Malaysia
SESSION TITLE: Measuring prices of real estate and its challenges
CHAIR: Toh Hock Chai, Bank Negara, Malaysia
• Jens Mehrhoff, Deutsche Bundesbank
Title: Commercial Real Estate Indicators: Prices and Beyond
Abstract: As the global financial crisis has impressively shown, changes in real
estate prices influence the health and soundness of the financial sector. However, the
effective monitoring of the markets is severely hampered by the lack of comparable
and reliable data. While good progress has been made as regards the compilation
and dissemination of housing price statistics, the compilation of commercial real
estate (CRE) price and associated indicators remains very challenging. Against this
background and in the context of the G20 Data Gaps Initiative, Eurostat publis-
hed in December 2017, under the auspices of the Inter-secretariat Working Group
on Price Statistics (IWGPS), a Statistical Report on ’Commercial Property Price
Indicators: Sources, Methods and Issues’ that makes a first attempt at setting out
the wide range of challenges linked to the measurement of CRE. This talk would
present, and invite to discuss, the current state of play as well as the way for-
ward on CRE price and associated indicators. This includes but is not limited
to: 1. data sources for commercial real estate statistics, such as the combination
of different administrative sources, if need be with specialised surveys; valuation
data from, say, investment funds or loan collateralisation; disaggregated data from
private sources; asking data or other big data sources; and stock-market based infor-
mation; 2. conceptual frameworks for commercial real estate indicators, examples
include how the interrelationship between prices, rents and yields (and potenti-
ally vacancy rates) can be exploited; the definition of commercial real estate; the
classification of properties in this domain; and aggregation and weighting issues;
and 3. purposes and uses of commercial real estate indicators and related targets,
comprising the monitoring of mainly disaggregated data rather than country-wide
level; the use of transaction or stock weights for price and associated indicators;
the inclusion or exclusion of rental housing in measures of CRE; and a review of
additional indicators on construction and transactions. To this end, this talk will
reflect upon the most recent developments at the European and the global level;
27
in particular, the follow-ups from the International Conference on Real Estate Sta-
tistics (https://www.real-estate-statistics.eu/) Eurostat organised, in close coopera-
tion with the ECB, in Luxembourg from 20 to 22 February 2019 that was preceded
by a G20 Workshop to advance implementation of Recommendation II.18 on Com-
mercial Property Price Indices (CPPIs) of the Second Phase of the G20 Data Gaps
Initiative (DGI-2).
• Nur Fazila Mat Salleh, Bank Negara Malaysia
Title: Enterprise Data Governance and Direction Going Forward
Abstract: Enterprise data governance comprises a set of policies, procedures
and processes, and standards to ensure that data are formally managed and utili-
sed throughout the organisation. However, enforcing a data governance framework
within an organisation is always not easy due to factors such as conflicting claims
on data ownership, increasing and changing demand of data by policy makers, and
need to balance the burden to reporting entities and compilers against the needs
of data users. The presentation aims to provide insights on the establishment and
implementation of data governance at Bank Negara Malaysia and how does it facili-
tate an end-to-end data management. It will also highlight the role and importance
of IT in supporting the governance framework, and how can we align business and
IT to achieve better data management.
• Soong Kim Loong, Maybank, Malaysia
Title: Data Governance: Adapting to the Digital Age
Abstract: The complexity of managing data has intensified with the onslaught
of exponential increase in data volume, speed and variety. We are increasingly
tasked to store and manage new data that we dont normally think of as traditional
reporting data such as web logs or geo-location data. Data that we manage are
extending beyond structured data that we are familiar with like relational databases.
More and more data are being generated by machine in addition to human. All
these challenges coupled with the expectation to turnaround data from creation to
analytics in the shortest possible time highlighted the need to pay more attention
to how we proactively manage and govern data at various critical data touch points.
This paper will discuss how data governance as a discipline has been increasingly
28
pushed to the forefront of data management and how do we adapt to the fast evolving
data landscape.
• Silke Stapel-Weber, European Central Bank
Title: ECB data governance for better analysis and decision-making
Abstract: Data are necessary to enable analysis and support decision-making.
The paper will outline the ECB data governance structure: how do we foster stra-
tegic alignment, standardisation, as well as collaboration. How do we address the
challenges to data access to exploit the wealth of data available within the ECB
whilst safeguarding confidentiality? And how do we best leverage the data exper-
tise available across business areas?
29
ORGANIZER: WeeYeap Lau, University of Malaya, Malaysia
SESSION TITLE: Applied statistics
CHAIR: Muzalwana Binti Abdul Talib, University of Malaya, Malaysia
• Muzalwana Binti Abdul Talib, Wan Katrun Nadia Wan Yusof,
University of Malaya, Malaysia
Title: Air Quality Monitoring with Exponentially Weighted Moving
Average (EWMA) control charts
Abstract: Air quality monitoring, testing, and measuring are becoming in-
creasingly sophisticated with the widespread adoption of technological equipment.
However, interpreting monitoring data and deciding when and how to apply envi-
ronmental management remains a subjective and underdeveloped area of research.
Control charts, as originally developed for industrial applications, offer some pro-
mises in this regard. Control charting scheme can provide an approach to identify
when a system is going out of control. When such signals identified, corrective
measures can be taken. This technique would be practical in the context of envi-
ronmental monitoring. Despite their potential utilization, control charts have rarely
been adopted in environmental management, especially in air quality monitoring.
There are very few literatures on similar study or have applied a broad range of
statistical process control methods, that includes control charts applications. Based
on these theoretical discussions and applications, this paper presents the develop-
ment of control chart scheme of air quality performance in the Central region of
Malaysia from 2010 to 2015. Two control charts are applied: x -R control chart and
Exponentially Weighted Moving Average (EWMA) control chart. EWMA control
chart are specifically plotted to detect subtler shift away from a mean trend and
hence provide a more consistent warning of the decline in air quality. We believe
that control chart could have clearly communicated this process earlier, enabling
the environmental personnel to make decision effectively.
• Ahmad Farid Osman, University of Malaya, Malaysia
Title: Forecasting weekly electricity demand in Malaysia using ex-
ponential smoothing approaches
Abstract: The study employs time series approaches to generate forecasts of wee-
30
kly electricity demand. In time series modelling, it is common to consider monthly
or quarterly seasonal effect. Due to change of weather and other environmental fac-
tors throughout the year, producing forecasts of electricity demand on quarterly or
monthly basis might not be sufficient to provide input for efficient management of
electricity supply. This study proposes modified modeling and forecasting procedure
that is suitable for weekly frequency of seasonality in the data with the use of two
time series approaches, namely, ETS and ESWR types of exponential smoothing
techniques.
• Siow-Li Lai and Tien-Ming Yip, University of Malaya, Malaysia
Title: The non-linear nexus between financial development and fer-
tility: A dynamic panel GMM analysi
Abstract: Fertility plays a vital role in regulating contemporary population
growth, in which mortality has reached a low level. Reducing fertility was seen
as a prerequisite to national development and poverty eradication in the 1960s/70s.
However, fertility far below replacement level in recent decades has given rise to con-
cern of population ageing and the emergence of labor shortage in the near future.
A comprehensive analysis of the forces affecting fertility is needed to provide the
necessary inputs to be used in development planning. This study aims to investigate
the relationship between financial development and fertility in 65 countries between
2001 and 2015. The dynamic panel GMM analysis was used to build the models
for fertility-financial development analysis, controlling for other variables such as
infant mortality rate, female education, and urbanization. Results show that finan-
cial development has a non-linear inverted U-shaped relationship with total fertility
rate in developing countries, but a non-linear U-shaped association is observed in
developed countries. This study draws some imperative policy recommendations.
31
ORGANIZER: Guodong Li, University of Hong Kong
SESSION TITLE: IASC invited session: Recent development in
complex dependent data
CHAIR: Lionel Truquet, ENSAI, France
• Liudas Giraitis, Queen Mary, University of London, UK
Title: Standard testing procedures for white noise and heteros-
cedasticity
Abstract: Commonly used tests to assess evidence for the absence of serial cor-
relation between time series in applied work rely on procedures whose validity holds
for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative
Ljung-Box tests can be significantly distorted. This paper adapts standard corre-
logram tests to accommodate hidden dependence and non-stationarities involving
heteroskedasticity, thereby uncoupling these tests from limiting assumptions that
reduce their applicability in empirical work. To enhance the Ljung-Box test for non
i.i.d. data a new cumulative test is introduced. Asymptotic size of these tests is
unaffected by hidden dependence and heteroskedasticity in the series. An exten-
sive Monte Carlo study confirms good performance in both size and power for the
new tests. Applications to real data reveal that standard tests frequently produce
spurious evidence of serial correlation.
• Lionel Truquet, ENSAI, France
Title: Time-varying time series models for categorical data
Abstract: We will present a general framework for modeling the dynamic
of categorical time series using autoregressive processes and which is compatible
with the inclusion of strictly exogenous covariates. The case of finite and infinite
dependence with respect to past values will be discussed as well as the possibility to
include time-varying parameters. Theoretical properties are justified either by some
techniques developed for studying Markov chains in random environments or by
some coupling methods for chains with complete connections. Our results also give
a theoretical basis for some observation-driven models introduced in econometrics
for studying the dynamic of recessions or price movements.
32
CONTRIBUTED SESSION 2
CHAIR: Wiji Tri Wilujeng, Price Directorate BPS, Indonesia
• Wiji Tri Wilujeng, Price Directorate BPS, Indonesia
Title: Advertised Landed House Price in Balikpapan
Abstract: Online advertised landed house price is easily available data. Research
schedule and computer time computation are our constraint to do this research.
From the sample set of 101 advertised landed house prices, 16 observations are
separated as leverage. Leverage is defined as an observation that has land area
more than 300 square meters or building area more than 400 square meters or both.
Covariance and variance are calculated then a matrix is constructed. Determinant
is calculated from covariance matrix in iterative fashion. Iteration is performed to
find minimum determinant. Bivariate Minimum Covariance Determinant [MCD]
is applied to online advertised landed house price data to identify non-Extreme
outlier. Houses in Perumnas, Rumah Wika, Balikpapan Baru, Gunung Sari Ilir,
and Sepinggan Baru are non-Extreme outlier in Balikpapan.
• Deja Firda, North Penajam District Statistical Office, Indonesia
Title: Bivariate Landed House Data using Minimum Area of Ellipse
Abstract: In this research we use housing data located in Balikpapan taken
from online advertised landed house price. We used 124 sample data of landed
house price, size of land, and size of building. Seven landed house are separated
because they are considered as leverage. Leverage is defined as data that is far away
from the bulk of the gro up of the data [Rousseeuw and Zomeren, 1990]. Minimum
area of an ellipse is used to find non-Extreme outlier. Houses in Prapatan Dalam,
Kruing Timur, Batu Ampar are considered as non-Extreme outliers. Minimum area
of an ellipse is used to find non-Extreme outlier. Houses in Tampak Siring [sector
3], Perumahan Pondok Karya Agung, Perum Tamansari, Bukit Mutiara (Wika) are
non-Extreme outliers.
• Astari Raihanah, Sarmi District Statistical Office, Indonesia
Title: Pearson product moment is used more than just measuring
correlation
Abstract: This research uses data from Bengkulu. Firstly from the sample set of
33
advertised landed house prices, 9 observations are separated because the prices are
more than one milliard in local currency. Secondly 11 observations are separated as
leverage. Leverage is defined as an observation that has foot print more than 400
square meters or floor area more than 200 square meters or both. Thirdly Pearson
product moment is applied to online advertised landed house price data to identify
non-Extreme outlier. Houses in Simpang SMP 4 and Sungai Rapat are considered
as non-Extreme outlier in Bengkulu
• Ang Siew Ling, Pooi Ah Hin, Sunway University, Malaysia and Ng
Kok Haur, University of Malaysia
Title: Improvement of Reserve Estimate Using Information on Sub-
classes of Insurance
Abstract: This paper aims to use the individual claims data for estimating
reserve in nonlife insurance. Suppose the claims data of individual customers con-
tain the additional information on their subclasses of insurance, apart from the
delay times in reporting the claims, delay times in payments and the corresponding
amounts of claims / payments. Each subclass may initially be represented by binary
codes. A mixture of two multivariate power-normal distributions and a degenerate
distribution is fitted to the observed vectors of variables consisting of the binary
codes, the sum insured, the claim and payment records until the present time, and
the outstanding claims, liabilities (OCL). When the subclass, the sum insured, and
the claim and payment records of a customer until the present time are given, a
conditional distribution of the OCL is derived from the fitted mixture distribution.
From the conditional distribution, a prediction interval for the OCL is obtained. It
is found that the average length of the prediction interval for the OCL is shorter
when the information on the subclasses of insurance is included in the analysis. The
conditional distribution of the individual OCL is later used to find the distribution
of the sum of the OCL over the customers in a company. The Provision of Risk
Margin of Adverse Deviation (PRAD) can then be obtained from the distribution
of the sum of the OCL
34
ORGANIZER: Chun Yip, The Chinese University of Hong Kong
SESSION TITLE: Advanced Methodological Contributions in Time
Series
CHAIR: Yuanbo Li, University of International Business and Economics, Beijing,
China
• Yuanbo Li, University of International Business and Economics, Be-
ijing, China
Title: Group Orthogonal Greedy Algorithm for Change-point Esti-
mation of Multivariate Time Series
Abstract: We propose a three-step method for the detection of multiple structural
breaks in piecewise stationary vector autoregressive processes. The number of the
structural breaks can be large and unknown. Moreover, the number and the location
of the breaks are not necessarily the same in diff erent components. The proposed
method is based on a connection between structural break problem and high dimen-
sional regression problem. With such connection, we develop a group orthogonal
greedy algorithm, originally from high dimensional variable selection context, for
efficient estimation of structural breaks. A high-dimensional information criterion
is proposed to detect different structural breaks in different components. We prove
the consistency of the estimators and provide Monte Carlo experiments for the finite
sample performance.
• Zhan Liu, Hubei University, China
Title: Fitting time series models for longitudinal surveys with non-
ignorable missing data
Abstract: We develop a new method for handling nonignorable missing data
in fitting time series models for longitudinal surveys. We assume the response
probability not only depends on auxiliary variables but also the current outcome and
the past outcome which are subject to missingness. To incorporate the nonignorable
missing mechanism, an observed likelihood estimation approach is proposed based
on the distribution of the observed part of the sample and the response probability.
Also, we derive a series approximation for the observed likelihood function to achieve
efficient computation. Results from simulation studies are presented to demonstrate
35
the usefulness of the proposed methodology. An empirical example based on a panel
study of income dynamics is provided.
• Chi Tim Ng, Chonnam National University, South Korea
Title: Real time prediction of irregular periodic time series data
Abstract: By means of a novel time-dependent cumulated variation penalty
function, a new class of real-time prediction methods is developed to improve the
prediction accuracy of time series exhibiting irregular periodic patterns, in particu-
lar, the breathing motion data of the patients during the robotic radiation therapy.
The proposed methods are designed so that real-time updates can be done efficiently
with O(1) computational complexity upon the arrival of a new signal without scan-
ning the old data repeatedly. The performances are tested via simulation under
models involving abrupt changes and gradual changes in mean, trend, amplitude,
and frequency.
36
ORGANIZER: Grazia Vicario, Politecnico of Turin, Italy
SESSION TITLE: Computer Experiments
CHAIR: Alessandro Fasso, University of Bergamo, Italy
• Matthias Hwai, City University of Hong Kong
Title: Gaussian Process Modeling and Optimization of Simulators
for Physical Systems
Abstract: Gaussian process (GP) emulators are typically constructed to replace
time consuming simulators of physical systems to expedite quantitative analysis that
depends on the functional relationship between inputs and outputs of the simulator.
In this talk, I will first introduce the idea of using GP models to approximate
partial differential equation models solved numerically by computer codes. Then, I
will present a real case study on optimizing the engineering design of a centrifugal
compressor based on a very time-consuming simulator.
• Xinwei Deng, Virginia Tech, USA
Title: Gaussian Process Models for Computer Experiments with
Quantitative and Qualitative Inputs
Abstract: Computer experiments with both qualitative and quantitative factors
attracts wide attentions. Analysis of such experiments is not yet completely resol-
ved. In this talk, we present some recent development Gaussian process to model
computer experiments with qualitative and quantitative factors. The proposed me-
thods consider the coefficient associated with the qualitative factor to be a varying
coefficient of the quantitative factors. It embraces a flexible structure of incorpora-
ting qualitative factors in modeling the complex systems of computer experiments.
The merits of the proposed method are illustrated by both numerical examples and
real-data applications
• Alessandro Fasso, University of Bergamo, Italy
Title: Gaussian Processes and LASSO for change detection of 4D
climate datasets
Abstract: This talk will discuss the Gaussian Process modelling and change
detection of temperature profiles from the Integrated Global Radiosonde Archive
(IGRA) which consists of global radiosonde observations dating back to 1905. Change
37
detection methods developed for radiosonde have a long history. In this paper, a
locally stationary 4D geostatistical model coupled with fused LASSO is used for
identifying changes.
38
CONTRIBUTED SESSION 3
CHAIR: Titi Kanti Lestari, BPS Statistics, Indonesia
• Tigor Nirman Simanjuntak and Sri Hartini Rachmad, BPS Sta-
tistics, Indonesia
Title: Gender statistics of household fisheries industry in Indonesia
Abstract: This study aims to examine and analyze the characteristics of hou-
sehold of fishery business based on gender in Indonesia. Sources of data used are
specifically from latest Agriculture Census 2013 data collected by the Central Bureau
of Statistics Indonesia. The data show that more than 90 percent of fish cultivators
are dominated by men for all types of aquaculture fish (tilapia, koi, shrimp windhu,
and seaweed). The majority of fish farmers are concentrated in productive age (25-
54 years) by 63 percent and 16 percent are 55 years and older. The education level
of women in the fish farming business is very low, the majority is only elementary
school graduates and even more than 60 percent are no schooling. Koi fish farmers
have the highest level of education. Given the koi fish aqua culture is a superior
product of exports, it requires special knowledge and skills to cultivate this type of
koi ornamental fish. The low participation of women in fish cultivation industry and
profession as fisherman shows that male dominance, tradition and culture in this
sector lead to small space for women. In addition, the position and status of Indo-
nesian women who have multiple roles, namely: as family caretaker and household
income support, not as the main breadwinner cause that low participation.
• Suryo Adi Rakhmawan, Ema Tusianti, Abdurrahman, BPS-Statistics,
Indonesia
Title: The gender gap on formal worker participation in Indonesia:
Trend and the way forward
Abstract: This research aims to analyse trend of gender gap on formal em-
ployment and influential factors of formal employment participation by gender. By
harnessing descriptive analysis this study reveals that gender gap exists in some
characteristics (location, economic sector, education level, and access to training).
Meanwhile, multilevel panel analysis shows that mean years of schooling, produc-
tive population number, per capita expenditure, training accessibility, GRDP and
39
manufacture share on GRDP significantly determine number of women and man
working on formal status. Share of manufacture on GRDP and education are the
highest contributor of women involving on formal works. Thus, increasing those
factors might reduce gender gap on formal work as a part of decent work for all
target.
• Erli Wijayanti Prastiwi, Karmila Maharani, BPS-Statistics, Indo-
nesia
Title: Women and technical efficiency analysis of the creative ma-
nufacturing industry in Indonesia
Abstract: The world is now facing the digital revolution especially in indu-
stry development that has been occurring since decades ago. Technology usage and
development have been raised dramatically in the past decade, making a fusion
that blurs lines between the physical, digital, and biological spheres. Several new
products, goods and services as well as job creations are emerged. One of them is
called creative economy, a fresh economic research area combining technology and
heritage at once. Indonesia as one of the most populous country in the world is
getting ready to develop creative economy while it is growing fast with very unique
case. Most of creative economy entrepreneurs are women by 54.96 percent in 2017
with domination of young and productive labor in age 25 59 years old. This paper
aims to measure the Technical Efficiency (TE) of creative manufacturing industry in
Indonesia 2017, the factors affecting the technical inefficiency effect (TIE) by using
Stochastic Frontier Analysis (SFA), and how women can contribute to the efficiency
enhancement. The data source of this research is from annual Small Medium Enter-
prises Survey held by Statistics Indonesia with classification for food and beverage
industry, fashion industry, and craft industry. The result shows that there is tech-
nical inefficiency in the three industries. It means that there is chance to achieve
optimum production with combination of current inputs. The factors significantly
affect the Technical Inefficiency Effect (TIE) of the three industry are identified.
The contributions of this paper are not only for the government but also for entre-
preneurs creative manufacturing Industry in Indonesia in as well as for those who
focus on women empowerment in creative economy especially in manufacturing in-
dustry. This paper provides other perspective about productivity measurement as
40
well as the implementation to improve the capability of the business process.
• Lilis Heri Mis Cicih, University of Indonesia, Sri Hartini, BPS-
Statistics, Indonesia
Title: Gender Mainstreaming in Fisheries’ Household in Indonesia:
Statistical Analysis
Abstract: Women play a significant role in fishing activities, but their role has
not been taken into account and involved in fisheries development. The general
objective of this research is to analyze the role of gender in fisheries households in
Indonesia. The specific research objectives are: 1. to know household characteris-
tics factors which is related to the role of gender in the fishery household; 2. to
know the socio-economic conditions; 3. analyze the relationship between household
characteristic factors which is related to the role of gender in the fishery household;
4. analyze the relationship between socio-economic conditions with decision making
in the household fishery. The research method used in this research is a combina-
tion of quantitative and qualitative methods. This combination is done to enrich
the data and better understand the social phenomena studied. Qualitative appro-
ach used in this research is descriptive case study method, to know the description
of research location and general description of respondents. Qualitative data was
obtained through in-depth interviews and direct observation at the research sites
to deliberate subjective understanding of the respondents. Quantitative approach
used is to process data survey and census results been collected by national statistics
Indonesia. This quantitative research is explanatory research explaining the causal
relationship between variables through hypothesis testing. The data gathering of
quantitative and qualitative simultaneously would exploit the phenomenon and ex-
pected to find best problem solve in the gender fisheries of Indonesia experience. The
result shows that household characteristics and socioeconomic factors are linked to
gender roles and decision making in fisheries households. The community attitude
may be influenced by work opportunity and culture through family education. The
coastal fishery development program which is gender sensitive has the most chance
and is considered important by stakeholders to be implemented is human resource
development program for male and female.
41
ORGANIZER: Sheng-Tsaing Tseng, National Tsing Hua University, Taiwan
SESSION TITLE: Reliability Modeling and Process Monitoring
CHAIR: Sheng-Tsaing Tseng, Institute of Statistics & National Tsing Hua
University, Taiwan
• Yi-Fu Wang, Department of Mathematics, National Chung Cheng
University, Chiayi, Taiwan, (co-authors: Sheng-Tsaing Tseng, Bo
Henry Lindqvist and Kwok-Leung Tsui)
Title: End of Performance Prediction of Lithium-ion Batteries
Abstract: Rechargeable batteries are critical components for the performance
of portable electronics and electric vehicles. The long term health performance of
rechargeable batteries is characterized by state of health which can be quantified
by end of performance (EOP) and remaining useful performance. Focusing on EOP
prediction, this paper first proposes an accelerated testing version of the trend-
renewal process model to address this decision problem. The proposed model is also
applied to a real case study. Finally, a NASA dataset is used to address the predic-
tion performance of the proposed model. Comparing with the existing prediction
methods and time series models, our proposed procedure has better performance in
the EOP prediction.
• Su-Fen Yang, Department of Statistics, National Chengchi Univer-
sity, Taiwan
Title: A New Loss Control Chart for Monitoring the Deviation of
the Quality Variable from the Target Value
Abstract: Almost all businesses strive to offer superior customers service. The
quality and loss of products are crucial factors among competitive businesses in glo-
bal market. Firms widely employ a loss function to measure the loss caused by poor
quality of products. The quality of products can be measured by the deviation from
the process target value from the Taguchis philosophy viewpoint. In reality, there
are many situations where the process distributions are not normal. This paper aims
at developing the loss-based control charts for monitoring the deviation of quality
variable from the target value under non-normal distribution. Numerical results
show the out-of-control detection performance of the proposed loss control charts.
42
Key words: Loss function, control chart, target value, non-normal distribution.
• Sheng-Tsaing Tseng, Institute of Statistics, David Shan Hill Wong,
National Tsing Hua University, Taiwan
Title: Misspecification Analysis of a pH Acceleration Model
Abstract: Shelf-life prediction of liquid-type (such as nano-sol) products is an
interesting research topic. Recently, a pH acceleration method has been proposed
in the literature to address shelf-life prediction of nano-sol. The time evolution of
particle-size distribution was obtained and modelled. There are two approaches for
modelling the particle-size distribution, either by using a sophisticated approach
(mixture-normal distribution) or a naive distribution-free approach. The main goal
of this study is to proactively quantify the seriousness of model misspecification on
the shelf-life prediction when the true particle-size distribution follows a mixture-
normal distribution, but wrongly treated it by a distribution-free model. The results
demonstrated that the relative bias of shelf-life prediction may be under-estimated
up to 13.66Keywords: Mis-specification analysis; Shelf-life prediction for Nano-sol;
pH acceleration model; Mixture-normal distribution; Nonparametric Model
43
ORGANIZER: Gopalakrishnan Asha, Cochin University of Science and Tech-
nology, India
SESSION TITLE: Applications of Dependent Data
CHAIR: Rahim Mahmoudvand, Bu-Ali Sina University, Iran
• Vincent Raja, University of Guyana, Guyana & Gopalakrishnan
Asha, Cochin University of Science and Technology, India
Title: Bivariate Discrete Distributions for Load Sharing Models
with Applications
Abstract: In reliability literature, lifetime data has been analysed by variety
of bivariate distributions. Most of the existing models deal with continuous failu-
res rates. Discrete failure rates quite often occur with situation where product or
organ lifetime can best be described through non-negative integer valued random
variables. Motivated by this, we propose a general class of bivariate discrete load
share model for two component parallel system. Both the components are assumed
with independent failure rates and the dependence occurs when one of the two com-
ponents fails. We study the general properties of the model. A particular example
using geometric as the baseline distribution and its characterisations have been stu-
died in detail. A Maximum Likelihhod estimation procedure is discussed. Two real
time data sets one of which is a diabetic foot ulcer data, are analyzed to show our
model applicability. We conclude with summary and discussion.
• Rahim Mahmoudvand, Bu-Ali Sina University, Iran
Title: Is the Exchange Rate Predictable in Long-Run: Experiences
from Iranian Currency?
Abstract: Exchange rates are among the most important economic indices that
have a big influence on the level of imports and exports, domestic and international
markets, and inflation. On the other hand, exchange rates are affected by many
highly correlated economic, political and even psychological factors. The interaction
of these factors is in a very complex fashion. Therefore, forecasting the changes
of foreign exchange rates is generally very difficult. However, obtaining a precise
prediction of exchange rate can be very useful for investors and government. In
this paper, we consider monthly exchange rate between the Iranian Rial (IRR) and
44
the US Dollar (USD). Since the beginning of 2018 IRR has depreciated by nearly
70 percent against its benchmark. Most experts believe this situation as the effect
of rising tensions in US-Iran relations, particularly the USs withdrawal from the
landmark 2015 Iran nuclear deal. However, we believe that there is a strong cyclic
behavior in IRR. We guess such behavior is due to the presidential change in Iran.
In order to assess our proposal, we examined the predictability of IRR/USD in long-
run using monthly data over the period 1981 to 2018. A preliminary analysis of the
original time series reveals a strong positive trend. For further analysis, we adjusted
the original time series for each presidential term by dividing exchange rate to the
first value of that period. The new time series reveal a strong negative trend and
harmonic components with frequencies 4 and 8 years. As a conclusion, the results
confirm that IRR/USD is predictable in long-run.
• Diana Abdul Wahab, University of Malaya, Malaysia
Title: Decomposition of Graduate’s Gender Wage Gap in the Public
and Private Sectors in Malaysia.
Abstract: This paper studies aspects related to the public-private sectors wage
determination for fresh graduates and the evidence of gender wage gap between and
within industries. Using the national data from the Tracer Study in 2013 involving
first degree graduates from higher learning institutions in Malaysia, the first part
discusses the determinants of the overall gender pay gap for all graduates where it is
found that graduate’s income is determined by their academic achievement, English
proficiency, family income, and specific types of courses they took in university.
The income difference between male and female graduates is computed using the
Oaxaca-Blinder (OB) decomposition. The results from separate ordered choice esti-
mation for each gender are decomposed into two portions: one portion represents
difference in wages between two graduates having different characteristics, where
we would expect individual with higher productive characteristics to earn more;
and one portion explains the difference in the earning among two graduates pos-
sessing similar characteristics and job attributes and hence may provide potential
evidence of discrimination. The average earning of public sector workers is found to
be slightly lower than private sector workers but not significant. Detailed analysis
of earning found that entry level earning between public and private sectors is not
45
significantly different, but men may earn more in the private sector while Malays
may earn more in the public sector. Higher earners in the public sector generally
have higher CGPA, indicating that graduates with better academic achievement
may seek public sector employment for its monetary reward. English language is
not an important element in the public sector earning determination. Business sub-
jects are rewarded more in the private sector, while graduates who took Sciences
subjects, Arts and Social Sciences may earn more if they work in the public sector.
46
CONTRIBUTED SESSION 4
CHAIR: Shinsuke Kamoto, Kagawa University, Japan
• Ani Budi Astuti, University of Brawijaya Malang, Indonesia
Title: Series of Activities for Increasing the Mount Bromo Tourism
Business in Indonesia through Modeling Nonlinear Principal Compo-
nent Analysis
Abstract: The Mount Bromo is one of the most exotic tourist destinations in In-
donesia and very famous so that it is visited by many domestic and foreign tourists.
The location of Mount Bromo is in East Java Province, precisely surrounded by four
regency government regions, namely Probolinggo Regency, Pasuruan Regency, Ma-
lang Regency and Lumajang Regency. Various series of activities from the tourism
management of Mount Bromo continue to be carried out in an effort to improve the
quality of tourism to maintain and increase visits of domestic and foreign tourists.
Through the Modeling Nonlinear Principal Component Analysis with mixed type
latent variables, we can know the main influences of various variables that determine
satisfaction and interest in visiting tourists at Mount Bromo. The purpose of this
study is to identify the main indicators of each mixed type latent variable as a va-
riable determinant of satisfaction and interest in visiting for Mount Bromo tourists
in Indonesia through Modeling Nonlinear Principal Component Analysis. The data
used in this study are primary data from respondents from Mount Bromo tourists
in Indonesia at 2018. The results showed that the mixed demographic latent varia-
bles with five indicators can be formed by a single nonlinear principal component
with the dominant contribution of indicators indicated by the last education and
age from tourists. The service quality latent variables with five indicators can be
formed by a single linear principal component with the contribution of the domi-
nant indicators in responsiveness and assurance. The marketing mix latent variable
with seven indicators can be formed by a single linear principal component with
the dominant indicator contribution to the process and promotion. The Electronic
Word of Mouth (e-WoM) latent variables with four indicators can be formed by a
single linear principal component with the contribution of the dominant indicators
on expressing positive feelings and concern for other.
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• Shinsuke Kamoto, Kagawa University, Japan
Title: Capacity expansion and financial leverage under a potential
entry threat
Abstract: The study examines capacity expansion strategy and leverage choice
of an incumbent firm in the presence of a potential entry threat by a new entrant.
The model presented in this paper is based on real options theory that examines in-
vestment decision under uncertainty over future market environments. It formulates
the capacity expansion strategy regarding its timing and size and leverage choice as
an optimal decision problem that maximizes the value of the firm under uncertainty
over a future product price. The problem is solved subject to the optimal response
to the potential competitors market entry decision. The model demonstrates that
an incumbent monopolist undertakes capacity expansion with the strategic motiva-
tion to delay the market entry by a potential competitor for prolonging a period
of monopoly status. The model also demonstrates the interaction between the in-
cumbents capacity expansion strategy and leverage choice under a potential entry
threat. In addition, the model demonstrates that high leverage of the incumbent
can induce the potential competitor to enter the market in the industry downturn
in order to force the monopolist to go bankrupt. This study contributes the lite-
rature in real options theory by examining interactions between strategic capacity
expansion strategy and financial leverage choices in the presence of a potential en-
try threat. The results presented in this study would provide useful insights into
strategic aspects regarding corporate investment and financing policies.
48
• Kwardiniya Andawaningtyas, University of Brawijaya Malang,
Indonesia
Title: Analysis of grouping ABC - VED and predicting the number
of requests
Abstract: Market competition requires a company to be able to optimize
the management of the company that are appropriate, effective and efficient. It is
considered very important because of the rapid development of business in this mil-
lennium era, so it can compete with other companies to be able to meet consumer
demand. To plan management at TB. Bina Usaha Temanggung Indonesia is used
ABC analysis to sort items that dominate sales based on investment value. VED
analysis aims to classify goods based on the level of importance of store owner’s
sales needs into 3 categories, namely Vital, Essential, and Desirable. Classification
of the drug using ABC VED analysis produce a matrix that differentiates into three
categories, namely category I is composed of AV, AE, AD, BV and CV; category II
consists of BE, CE, and BD; and the third category consists of CD. The results of
the ABC-VED analysis are then predicted the number of requests for goods with
the Double Exponential Smoothing method from Brown. This forecasting method
is used when the data indicate a trend. Trend is a smoothed estimate of average
growth at the end of each period. Forecasting results are expected to help plan in-
ventory for the coming year at TB. Bina Usaha Temanggung Indonesia to prioritize
sales.
• Fethi Ozbek, Turkish Statistical Institute, Turkey
Title: Poultry industry production statistics in Turkey
Abstract: Poultry industry production statistics cover annual and monthly
studies. The aims of the study are to monitor the developments in the economic
structure of Turkey, to compile data sets for monitoring the status of poultry hol-
dings, to obtain the information used for economic and social measures taken by
decision makers, to prepare data for various studies for defining poultry policies, to
define the share of sector in national income, to compile data in coherent with Eu-
ropean Union animal production legislation. Poultry and eggs production statistics’
studies are only conducted to poultry holdings in industry (excluding household
poultry farms). The following variables are produced; hen eggs, eggs placed in in-
49
cubation for chicks of broiler, number of hatched chicks for broiler, eggs placed in
incubation for chicks of laying hen, number of hatched chicks for laying hen, eggs
placed in incubation for chicks of turkey, number of hatched chicks for turkey, eggs
placed in incubation for chicks of quail, number of hatched chicks for quail, slaugh-
tered broiler, broiler meat, slaughtered turkey, turkey meat, slaughtered quail, quail
meat. Seasonally adjustment methods have been applied for hen eggs production,
slaughtered chicken numbers, produced chicken meat, slaughtered turkey numbers,
and produced turkey meat since 2014. Seasonally adjusted data have been publis-
hed monthly. The seasonal adjustment of poultry production statistics is carried
out using TRAMO-SEATS methodology. The software that is used for the applica-
tion of this method is 2.2.0 version of JDemetra+ developed by the National Bank
of Belgium (NBB) in cooperation with the Deutsche Bundesbank and Eurostat in
accordance with the Guidelines of the European Statistical System.
50