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Applied and Computational Statistics

Applied Statistics involves the development of techniques or models and also involves the use of statistical methodology to solve applied problems both in the field of Statistics and various disciplines such as Economics, Environment, Agriculture, Biology, Medicine, Engineering and others.

Computational Statistics are computer intensive methods to solve existing statistical problems computationally.

The field of Applied Statistics and Computational Statistics comprises the following sub-fields:

1)     Statistical Modelling and Forecasting

2)     Computational Statistics and Inference

3)     Reliability Analysis
 
A detailed description of these sub-fields are as follows:

1)  The sub-field of Statistical Modelling and Forecasting encompasses Time Series Analysis, Forecasting, Spatial Modelling, Extreme Value Theory, Envirometrics, Regression Analysis and Design of Experiments. Time Series is a sequence of random variables observed over an indexed set. Time series research is centered on Generalised ARMA (GARMA) models. In spatial modeling, research is focused on FISSARMA and integer- valued spatial models. In extreme value theory, research is focused on spatial extreme and environmental models. Research in regression analysis is focused on diagnostics, robust and nonparametric techniques and logistic regression. Diagnostics, Generalized Linear Model for designed experiments with non-normal response and robust techniques in Design and Analysis of Experiments is also the current research area.

2) This sub-field of Computational Statistics and Inference comprises Robust Statistics, Influence Diagnostics, Bootstrapping, Bayesian Statistics, Monte Carlo Markov Chain (MCMC) and Statistical Data Mining. In robust statistics, the research is focused on robust regression, outlier detection, estimation of parameters in linear and non-linear models and quality control techniques. A robust bootstrapping technique is another focus area of research. Research interest in Bayesian statistics focuses on prior information, formulation and solution of simple and complicated decision theory problems. The MCMC research ifocuses on the MCMC algorithm for performing Bayesian inferences on complex stochastic models .In statistical data mining, the research is focused on developing new inferential techniques in data mining through the optimization technique, bagging and boosting.

3) The sub-field of Reliability Analysis consists of four areas of research, which are Survival Analysis, Medical Statistics, Biostatistics and Quality Control. Survival analysis is a branch of statistics dealing with death in biological organisms and failure in mechanical systems. More generally, survival analysis involves the modeling of time to event data. Medical statistics is the field of medicine dealing with applications of statistics to the field of health and medicine, such as public health, forensic medicine, as well as clinical sciences.Biostatistics is the application of statistics to a wide range of areas in biology that leads to a particular application to medicine and to agriculture. Research is focused on Relative Survival, Cure Fraction, Survival Tree and Bayesian Survival Analysis.

 

Senarai Penyeli

 

Prof. Dr. Habshah Midi

Tel: +603.9769.6606
Email: habshah@upm.edu.my

Link:
Scopus | Google Scholar ResearchGate

2

Prof. Dr. Noor Akma Ibrahim

Tel: +603.9769.6847
Email: nakma@upm.edu.my

Link:
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3

Assoc. Prof. Dr. Mohd Bakri Adam

Tel: +603.9769.6860
Email: bakri@upm.edu.my

Link:
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Assoc. Prof. Dr. Mohd Rizam Abu Bakar

Tel : +603.9769.6824
Email : mrizam@upm.edu.my

Link:
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5

Prof. Madya Dr. Jayanthi A/P Arasan

Tel: +603.9769.6835
Email: jayanthi@upm.edu.my

Link:
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6  

Dr. Mohd Shafie Mustafa  

Tel: +603.9769.6817
Email: mshafie@upm.edu.my

Link:
Scopus | Google Scholar ResearchGate

 7  

Dr. Norhaslinda Ali

Tel: +603.8946.6828
Email: norhaslinda@upm.edu.my

Link:
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Updated:: 12/08/2020 [noorizzati]

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