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Courses


  Two-year M.Sc. Course

  M.Phil in Statistics and Computer Applications


M.Phil in Statistics and Computer Applications

Part I Examination (50 marks X 4 papers = 200 marks):

Paper I (20 credit points and 50 marks): 

Review of candidates research topic on which he/she has to do the dissertation (5 credits), Seminar presentation (5 credits), Seminar attendance (2 credits), Research methodology (5 credits), and Statistical computing (3 credits)

[This course will run simultaneously with the course work for candidates enrolled for Ph. D  in Statistics from this University]

Paper II and Paper III (each of 20 credit points and 50 marks):

Any two out of the following five options (to be decided by the Departmental Committee for each academic session):

1.  Advanced Inference: Equivariance and Invariance; Multiple Testing and Simultaneous Inference; Measures of Association in Multiple Classification; Robust Procedures; Construction of some multivariate tests; Concepts of Union-Intersection Principle

References

JD Gibbons and S Chakraborti                           :           Nonparametric statistical inference

EL.Lehmann and J P Romano                            :          Testing statistical hypotheses

EL Lehmann and G Casella                               :          Theory of point estimation

J Shao                                                             :          Mathematical statistics

MS Srivastava & CG Khatri                                :          An introduction to multivariate statistics

TW Anderson                                                   :          An introduction to multivariate statistical analysis

FR Hampel, EM Ronchetti, PJ Rousseeuw         :      Robust statistics: The approach based on and WA Stahel influence functions

2.       Applied Multivariate Analysis:  Multivariate graphical displays, Tests and plots for model and distribution validation, Multivariate outlier detection, Principal Component Analysis and associated plotting techniques (Bi-plots and h-plots), Multidimensional scaling, Classification and Regression Trees (CART), Conjoint Analysis.

References:

T Hastie, R Tibshirani, and J Friedman     :          Elements of Statistical learning

RJ.Muirhead                                          :           Aspects of Multivariate Statistical Theory

MS Srivastava & CG Khatri                     :           An Introduction to Multivariate Statistics

D.F. Morrison                                        :           Multivariate Statistical Methods

WR Dillon and M Goldstein                     :           Multivariate analysis (Methods and applications)  

RA Johnson and DW Wichern                 :           Applied multivariate statistical analysis

GAF Saber                                            :           Multivariate observations

BK Orme                                              :           Getting started with conjoint analysis

JJ Louviere                                            :           Analyzing decision making: Metric conjoint analysis

W Hurdle and L. Simar                           :           Applied multivariate statistical analysis

Advanced Regression Analysis: Nonparametric methods in regression, Shrinkage estimator, Bayesian analysis in regression, some variations in the standard regression models, Generalized Additive Models, Generalized Linear Mixed Models, Robust regression.

References:

N Draper & H Smith                              :           Applied regression analysis

HD Vinod & A Ullah                              :           Recent advances in regression methods

P McCullagh & AJ Nelder                      :           Generalized linear models

CE McCullough & SR Searle                 :           Generalized, linear and mixed Models, 2nd ed.

J Rousseeuw  & AM Leroy                    :           Robust regression  & Outlier detection

T Hastie and R Tibshirani                      :           Generalized additive models

4.       Applied Stochastic Process and Time Series Analysis: Markov chains in population genetics; Epidemic models; Financial processes; State space model and Frequency domain analysis in time series

References:

A Goswami and BV Rao              :           A course in applied stochastic process

W Paul and J Baschnagel           :           Stochastic processes: From physics to finace

AN Shiryaev and N Kruzhilin       :           Essentials of stochastic finance: facts, models and theory

V Capasso and D Bakstein        :           An introduction to continuous time stochastic Processes: Theory, models and applications to finance, biology and medicine

H Andersson and T Britton         :           Stochastic epidemic models and their statistical analysis 

GEP Box, GM Jenkins and GC Reinsel : Time series analysis: Forecasting and control

PJ Brockwell and RA Davis        :           Time series: Theory and method

RS Tsay                                   :           Analysis of financial time series

NH Chan                                  :           Time series: Applications to finance

WA Fuller                                :           Introduction to statistical time series

 

Advanced Designs of Experiments and Sample Surveys:

Advanced Designs of Experiments:

Optimum Experimental Designs: Optimality of standard designs (RBD, BIBD, BBD, LSD and YSD); Optimum Regression Designs: Optimum designs for polynomial regression in a single variable; Robust Designs: Robust designs against non-normality; presence of outliers etc., Robust parameter designs (Taguchi)

References:

J Kiefer                            :          Construction and optimality of generalized Youden designs. In A Survey of Statistical Design and Linear Models ( J.N. srivastavs ed.), pp 333-353

KR Shah and BK Sinha     :           Theory of Optimal Designs

F Pukelsheim                   :           Optimal Design of Experiments

DC Montgomery                :           Design and Analysis of Experiments 

RH Myers and DC Montgomery :    Response Surface Methodology

Advanced Sample Surveys:

Variance estimation in survey sampling; Estimation for domains and small area estimation; Network sampling and adaptive sampling; Randomized response surveys.

References:

CR Särudal, B Swenson and J Wretman           :  Model assisted survey sampling

SK Thompson                                                 :  Sampling, 2nd edition

A Choudhuri and R Mukerjee                            :  Randomized response: Theory and techniques

JNK Rao                                                         :  Small area estimation    

Paper IV (20 credit points and 50 marks): 

Computer Application:  Linux Operating System; Data Base Management System; Data Mining techniques; Decision trees; Software Quality and Reliability.

Part II Examination (100 marks + 150 marks + 50 marks = 300 marks):

Part II A:  Internal assessment (100 marks (40 credits) based on Review essays and at least 2 Seminars

Part II B: Dissertation (150 marks (60 credits)) and Comprehensive Viva voce (50 marks (20 credits))

 

Department of Statistics University of Calcutta 35, Bullygunge Circular Road,PIN -700 019,Kolkata (Calcutta),West Bengal,INDIA .Phone: +91 (33) 2475 3681/82 Fax: +91 (33) 2476 4419