Keynote & Invited Speakers

Keynote Speaker

Keynote Speaker I

Dr Nuanpan Lawson

Associate Professor Dr Nuanpan Lawson
King Mongkut's University of Technology North Bangkok, Thailand

Title: Adjusting For Nonresponse in the Analysis of Sample Survey Data

Abstract: Sample surveys are often subject to non-response, and exhibit cluster-level association as well. In this research, we study how to fit a linear regression model at the cluster-level when non-response occurs on the study variable.  We consider the relationship between response rates and the survey variables at the cluster-level. We propose an alternative approach to find the estimated values of regression coefficients under two underlying models for non-response, while the usual regression coefficients are estimated by ignoring non-response. An example is provided to illustrate the properties of these estimators: a simulation study from a survey of employees that includes both non-response and clusters consisting of workplaces.

Keynote Speaker II

Dr Adibah Shuib

Associate Professor Dr Adibah Shuib
President of the Management Science/Operations Research Society of Malaysia (MSORSM)

Deputy Director (Research & Education)
Malaysia Institute of Transport (MITRANS)
Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

Title: An Optimization Model for Airport Aeronautical Revenues

Abstract: Maximising aeronautical revenues is one of the greatest challenges for airports, particularly regional airports. Daily operational factors may have different influences towards aeronautical revenues generated for the airport. This paper discusses the development of the Aeronautical Revenue Optimisation Model (AROM) with input data concerning mode of operations, traffic types, flights details, fleet types and type of flights in order to determine the composition of flight operations of the airport that produces optimum aeronautical revenues that could be achieved. A baseline matrix, established using the Bayesian Network (BN) method, analyses the impact of particular action related to factors identified and associated uncertainties which could provide valuable information to airport planner or manager in determining strategies based on the probability calculated for potential aeronautical revenues that could be generated. Among the main elements that generate aeronautical revenues are the aircraft landing charges and airport taxes. AROM is formulated as a mathematical programming model with two objective functions, to maximize the aeronautical revenues from the arriving and departing flights. Results obtained show that the maximum aeronautical revenue achieved depends on certain flights composition, which specify the flight types with higher frequency in contrast to the current practise of offering small number of mixed flight types. The proposed model offers flexibility to decision makers in setting the bounds of the flights’ constraints. The model can be extended to include more variants of the airside operations factors and generalised to consider domestic or international traffics or both. The traffic types can also be adjusted to include shorter or longer list of flight types or different types of aircraft.

Keynote Speaker III

Dr Suhartono

Associate Professor Dr Suhartono
Department of Statistics
Institut Teknologi Sepuluh Nopember

Title: MGSTAR: An Extension of the Generalized Space-Time Autoregressive Model

Abstract: Up to now, Generalized Space-Time Autoregressive (GSTAR) models are mostly focused only for univariate spatial-temporal data. This research proposes an extension of GSTAR for multivariate spatial-temporal data, known as Multivariate GSTAR or MGSTAR. Three studies were conducted in this research, i.e., theoretical, simulation, and applied studies. These studies were initially developed based on bivariate spatial-temporal data. A theoretical study was done by developing MGSTAR based on the framework of Vector Autoregressive (VAR) models. In this proposed MGSTAR model, the parameter estimation was obtained by implementing Ordinary Least Square (OLS) method. The simulation study showed that OLS method yielded unbiased estimator. Furthermore, the MGSTAR models have applied for forecasting CO and PM10 at three stations in Surabaya City. The results showed that MGSTAR model could explain well the dynamic relationship between variables and locations. However, based on Root Mean Square Error Prediction (RMSEP), the results showed that MGSTAR model yielded less accurate forecast than univariate ARIMA model due to MGSTAR employed simpler order of Autoregressive. Further research is needed to expand the MGSTAR model with a higher order of Autoregressive, particularly to handle trend and seasonal order.

Invited Speaker

Invited Speaker I

Dr Daisuke Sasaki

Assistant Professor Dr Daisuke Sasaki
International Research Institute of Disaster Science (IRIDeS)
Tohoku University, Japan

Title: Possibility of Utilizing Disaster Statistics

Abstract: At the Third UN World Conference on Disaster Risk Reduction (UNWCDRR) held in March 2015 in Sendai City, Japan, the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) containing seven global targets was adopted by 187 UN member states. Monitoring and reporting on the progress in achieving these global targets is mandatory, thus, more scientific and statistical analysis has been needed than ever. The Global Centre for Disaster Statistics (GCDS) in Tohoku University was established in April 2015 to support the SFDRR in the monitoring and evaluation of progress by providing support at country level for capacity building in developing national statistics on disaster damage and by establishing an improved global database for such statistics. The study focuses on the possibility of utilizing disaster statistics for implementing the SFDRR effectively and efficiently and how the GCDS could contribute to the literature of disaster statistics.

Invited Speaker II

Prof. Chia Gek Ling

Professor Chia Gek Ling
Department of Mathematical and Actuarial Sciences
Universiti Tunku Abdul Rahman

Title: How to Square A Square?

Abstract: "Can a square be cut into smaller squares no two of which have the same size?"; This problem, which dates back to around 1925 has resisted the efforts of many who attempted to solve it until four students from Cambridge University made an attack on it (in the years 1936 - 8) by translating it into an electrical-network problem (equipped with graph theory). In this talk, we give a brief account on how these students finally squared the square.