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Master of Science in Statistics

The Institute of Mathematical Sciences, formed in 1998, started off as Department of Mathematics in 1959. Today, it is one of the largest mathematical institutes in Malaysia. The institute aims to:

  • Provide quality education and training in various areas of mathematics;
  • Nurture talent in the mathematical sciences and to produce graduates with problem-solving abilities to meet the challenges of an ever-changing world;
  • Strive for academic excellence via research and the dissemination of knowledge.

The academic staffs of the Institute are engaged in a broad spectrum of mathematical research, ranging from the highly abstract to areas with real-world applications. There is strong collaborations with international institutions such as: University of New South Wales, University of Ljubljana, UC Santa Cruz, Arizona State University, University of Oklahoma, University of Maine, Exeter University, Flinders University, Murdoch University and Northern Territory University, Simon Fraser University, University of Victoria and Jilin University. Apart from being members of several professional organizations, members of the Institute also serve as referees to various international journals.


Course Structure

At the end of the program, graduates with MSc. Statistics are able to:

  1. Comprehend advanced statistical theory covering statistical and mathematical arguments, proofs and abstract concepts.
  2. Apply technical and practical skills relating to advanced statistics required in different fields.
  3. Perform social responsibility with the skills in statistics.
  4. Practice professionalism and statistical ethics required in different areas such as management and medicine.
  5. Work in a group as a leader in dealing with statistical tasks.
  6. Solve statistical problems with scientific experience and skills.
  7. Use statistics in information management that contributes to lifelong learning.
  8. Practice efficient and effective management and entrepreneurial skills.

 

Master of Science in Statistics

Session 2017/2018

(42 CREDITS)

1. Program Core Courses (26 CREDITS)

Course Code

Course Name

Credits

SQB7001

Research Methodology for Statistics

3

SQB7002

Research Project for Statistics

10

SQB7003

Statistical Inference

4

SQB7004

Probability Theory

4

SQB7005

Statistical Laboratory

2

SQB7006

Statistical Consultancy and Data Analysis

3

2. Program Elective Courses (16 CREDITS)

Course Code

Course Name

Credits

SQB7007

Multivariate Analysis

4

SQB7008

Stochastic Models

4

SQB7009

Bayesian Statistics

4

SQB7010

Decision Statistics

4

SQB7011

Generalized Linear Models

4

SQB7012

Experimental Design and Quality Engineering

4

SQB7013

Statistical Time Series

4

SQB7014

Risk Theory

4

SQB7015

Stochastic Processes in Finance

4

SQB7016

Computer Intensive Methods

4

SQB7017

Robust Statistics

4

SQB7018

Statistical Methods in Bioinformatics

4

SQB7019

Data Mining

4

SQB7020

Survival Data Analysis

4

SQB7021

Epidemiology Modelling

4



SQB7001 Research Methodology For Statistics
This course is designed to give knowledge and skills to students related to suitable methodologies in statistical research. It includes searching and critically evaluating journal articles on statistical problems. The course will introduce fundamental statistical concepts and techniques useful for research in statistics. The students will be guided in writing research proposal in the area of statistics.

Assessment Methods:
Continuous Assessment 100%

Medium of Instruction:
English


SQB7002 Research Project
Refer to lecturers concerned for project synopsis and reference texts.

Assessment Methods:
Continuous Assessment 100%

Medium of Instruction:
English

Transferable Skills:
Skill in preparing a research proposal and report, programming skills

SQB7003 Statistical Inference
Principles of data reduction. Sufficient statistic. Factorization theorem. Minimal sufficient statistic. Lehman-Scheffe Theorem. Ancillary and complete statistics. Basu’s Theorem. Exponential class of distributions.

Likelihood ratio test. Union-intersection and intersection-union tests. Neyman Pearson Lemma and its generalization. Most powerful test. Unbiased test. Locally most powerful test. Asymptotic distribution of the likelihood ratio. Sequential probability ratio test.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-


SQB7004 Probability Theory
Introduction to basic concepts, probability measure and space, sigma-fields.  Random variables, measurability. Distribution functions. Generating functions, characteristic functions. Various modes of convergence of sequences of random variables. Classical limit theorems. Examples of applications of basic results.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-

SQB7005 Statistical Laboratory
Introduce statistical packages such as S-PLUS, Minitab, SPSS and/or R. Use of functions and commands in statistical packages for exploratory data analysis, modelling and statistical inferences. Coding in a statistical programming language.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Programming skills

SQB7006 Statistical Consultancy and Data Analysis
Introduction to consulting methods. Related problems and issues. Exposure to the use of secondary data from various sources. Application of suitable statistical methods such as analysis of multivariate data, regression and time series in the analysis of real data. Producing reports and presenting the findings that are suitable for the needs of the statistical practitioners. Introduction to consultancy activities.

Assessment Methods:
Continuous Assessment 100%

Medium of Instruction:
English

Transferable Skills:
Ability to analyse real data using statistical software and to produce reports

SQB7007 Multivariate Analysis
The use/application of multivariate analysis. Managing and handling multivariate data. Matrix theory. Random vectors and matrices. The Multivariate normal and Wishart distributions. Selected topics from graphical methods, regression analysis, correlation, principal components, factor analysis, discriminant analysis and clustering methods.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-

SQB7008 STOCHASTIC MODELS
Poisson processes, backward and forward Kolmogorov equations, birth and death processes and examples. Definition and concepts in renewal processes, distribution for the number of renewal, renewal function and theorems for renewal processes.

Backward and forward renewal times. Examples for various types of renewal processes. Examples of applications of the theory in renewal processes.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-


SQB7009 Bayesian Statistics
Different functions relevant to Bayesian statistics, calculation of E (x) and Var (x).  Hypothesis testing of proportion, mean for posterior distribution, choice of sample size. Sufficient statistics and efficiency.  Bayesian estimators and properties of estimators.  Loss function, Bayesian risk.  Decision theory on x2 , subjective information compared to objective information.  Bayesian decision criterion.  Expected opportunity loss (EOL).  Bayesian inference - Beta-Binomial, Uniform prior, Beta prior, conjugate family, Jeffrey’s prior.  Choosing the prior Beta-Binomial - with vague prior, with conjugate prior information, choosing prior when you have real prior knowledge, constructing a general continuous prior, effect of prior. Bayes’ theorem for Normal mean with discrete and continuous prior.  Flat prior density (Jeffrey’s prior) for Normal mean.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-

SQB7010 Decision Statistics
Introduction to decision theory, risk function, non-randomized rules, randomized rules, completeness.  Bayes rules and minimax. Comparison of decision theory and classical theory.  

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-


SQB7011 Generalized Linear Models
Generalized Linear Models based on exponential family. Regression models like binomial (logistic) and Poisson in detailed. Statistical software for data analysis.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Ability to analyse data using statistical software

SQB7012 Experimental Design and Quality Engineering
Factorial designs. Blocking and confounding. Fractional factorial designs and its resolution. Supersaturated design. Response surface method and design. Robust parameter design.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Data analysis skills 

SQB7013 Statistical Time Series
Basic concept of model-building. Stationary model linear. Autoregressive and moving average model (ARMA). ARMA process includes identification, estimation, fitting and prediction. Non-stationary and seasonal model. GARCH model. Multivariate time series. State-space model.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Data analysis skills

SQB7014 Risk Theory
This course will emphasize on the applications of probabilistic models in the risky business, especially in insurance using the theory in stochastic processes. The topics include: individual risk model, computation of net premium, security loading and reinsurance, utility theory and its applications in reinsurance, collective risk model, number of claims distribution, aggregate claims distribution, surplus process and ruin probabilities.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
Malay/English

Transferable Skills:-

SQB7015 Stochastic Processes in Finance
Brownian motion and Ito’s lemma. Evaluation of option and future prices using Martingale and risk-neutral probabilities.

Black-Scholes formula, Stochastic interest rate and volatility.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-

SQB7016 Computer Intensive Methods
Error in floating point calculations. Probability function and distribution function approximations. Generating random numbers, including evaluating the quality of the generator and calculation methods in linear algebra: Gaussian elimination, sweep operators. Calculation methods for multiple regression, (not constrained) nonlinear regression and model fitting other than the least squares, bootstrap and Markov chain Monte Carlo.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Ability to analyse data using statistical software and programming skill

SQB7017 Robust Statistics
Foundation for Robust methods:

Tooth for judging robustness, measures of location and inference functions, measures of scales, M-estimation of location.

Some outlier detection methods.
Inference statistics: Construction of confidence intervals.
Robust regression estimation M, GM, MM estimation dan inference functions.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Programming skill

SQB7018 Statistical Methods in Bioinformatics
Statistical modelling of DNA/protein sequences: Assessing statistical significance in BLAST using the Gumbel distribution; DNA substitution models; Poisson and negative binomial models for gene counts; Hidden Markov Model.

Algorithms for sequence analysis and tree construction: Dynamic programming for sequence alignment and Viterbi decoding; neighbour-joining, UPGMA, parsimony and maximum likelihood tree-building methods.

Analysis of high-dimensional microarray/RNA-Seq gene expression data: Statistical tests for detecting differential expression, feature selection, visualization, and phenotype classification.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Computer Programming Linux OS

SQB7019 Data Mining  
Introduction to statistical methods and tools for analysing very large data sets and search for interesting and unexpected relationships in data.
Data Measurement: Types of measurements, distance measure, data quality.
Data reduction: Data organisation and display; Principal component, multidimensional scaling.

Data Analysis and uncertainty: Handling uncertainty; statistical inference; sampling

Data mining Algorithms: Classification and clustering – CART; artificial neural network; support vector machine; mining ordered dependence.

Modelling: Model Structure; curse of dimensionality; score function; optimisation methods; descriptive modelling and prediction. Data organisation.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Computer programming

SQB7020 Survival  Data Analysis 
Basic concepts such as survival and hazard functions. Survival data analysis including life table, Kaplan-Maier; log-rank and Wilcoxon tests. Survival regression modelling including the Cox regression model, several parametric models and the accelerated life time model and risk model. Diagnostic checking of the models. Application to the real dataset.

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:
Skills in analyzing medical data sets 

SQB7021 Epidemiology Modelling
Measures of disease frequency and risk. Assess strengths and limitation of different sources of epidemiology data.

Principles of study design: cross-sectional, cohort, case-control and intervention studies. Interpretation of epidemiology studies; causality, random errors, bias, confounding.

Regression methods for case-control studies. Unconditional and conditional logistic regression. Regression methods for cohort studies.  Issues in the case-control and cohort studies.

Evaluating published papers on epidemiology studies.                

Assessment Methods:
Continuous Assessment 50%
Final Examination 50%

Medium of Instruction:
English

Transferable Skills:-

 

Language Requirement
For international students, candidates are required to have TOEFL results at least 550 or IELTS at least  Band 5.5
Entry Requirement

Applicants must have a Bachelor's Degree with Honors CGPA 3.0 and above or equivalent in the relevant field.

Applicants with a Bachelor's Degree of CGPA 2.7 to 2.99 may be considered if they meet at least one of the following criteria:

A. Having relevant work experience; or

B. Produce publications in related fields; or

C. is a recipient of a scholarship; or

D. is a graduate of the University of Malaya; or

E. is a government servants

Applicants with a Bachelor's Degree of CGPA 2.5 to 2.69 may be considered if they meet at least two of the criteria in (1) (A) to (E) above

Fees
Kindly refer at Fees Structure
Last Update: November 20, 2017

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