DESCRIPTION& OBJECTIVES

The aim of this course is to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in research. Content includes: review of the key concepts of estimation, and construction of Normal-theory confidence intervals; frequentist theory of estimation including hypothesis tests;  methods of inference based on likelihood theory, including use of Fisher and observed information and likelihood ratio; Wald & score tests.

To produce the students, that has applicable knowledge about Inferential Statistics, which they apply in different fields. 

  1. To impart applied knowledge about Inferential Statistics and its tools
  2. To impart skills on the data collection, description measures of data interpretation of results, and decision making

INTENDED LEARNING OUTCOMES

After successfully completing the course, students will be able to:

Understand the philosophy and basic concepts of interval estimation and testing of hypothesis. Conduct appropriate interval and hypothesis tests for comparing population parameters. Demonstrate the ability to derive power functions and design of uniformly most powerful tests for continuous and discrete variables. Demonstrate understanding of the theory of maximum likelihood ration tests, sequential tests. Exhibit skills in interpreting and communicating the results of statistical analysis, orally and in writing.

CONTENTS

Neyman-PearsonTheorem, Uniformly most powerful tests, Likelihood ratio tests,

the sequential probability ratio test, Interval estimation and confidence tests.

Relation between testing and confidence intervals, asymptotic testing,

estimation, confidence intervals, optimality criteria, consistency.

  1. Hogg,A.V., McKean, J.W., and Craig, A.T. (2005). Introduction to Mathematical Statistics 6th ed. Pearson Prentice Hall, USA,
  2. Casella, G., and Berger, R. L. (2002) Statistical inference. 2nd  ed. Duxbury Press, CA, USA.
  3. Mood, A. M. Graybill, F. A. & Boes, D.C. (1997). Introduction to the Theory of Statistics, McGraw Hill,

Distribution of Marks:
Mid Exam:           30
Final exam:         50

Sessional (Assignment,Presentation,Participation,Attendance,Quizes)    20
Scheduled on:      

Msc 4:           Monday (12:00-01:00)    Tuesday (12:00-01:00)      Wednesday    (08:00-09:00)

 

Week

Topics and Readings

Books with page no

1.

Neyman-Pearson Lemma, its examples

Hogg and Craig

419-428

2.

Power functions, uniformly most powerful tests.

Hogg and Craig 419-428

3.

Deriving tests of Hypothesis concerning parameters in normal,.              

Hogg and Craig419-428

4.

Deriving tests of Hypothesis concerning parameters in exponential, gamma and its examples

Hogg and Craig

437-447

5.

Deriving tests of Hypothesis concerning parameters in uniform distributions.

Hogg and Craig

437-447

6.

Randomized Tests

Hogg and Craig 437-447

7.

Unbiased tests

Hogg and Craig 437-447

8.

Likelihood ratio tests and their asymptotic properties

Hogg and Craig437-447

9.

Sequential Tests: SPRT and its properties

Hogg and Craig 448-454

 

10.

A.S.N.  functions

 

Hogg and Craig 448-454

11.

 O.C. functions

Hogg and Craig 448-454

12.

Interval Estimation: Pivotal and other methods of finding confidence interval

Hogg and Craig

245-250

13.

Other Than pivotal methods of finding confidence interval

Hogg and Craig 245-250

14.

Confidence interval in large samples

Hogg and Craig 251-260

15.

Shortest confidence interval, optimum confidence interval. Bayes’ Interval estimation

Hogg and Craig

251-260

16.

Tests of Hypotheses: Simple and composite hypotheses, critical regions.

Hogg and Craig

263-272

 

Course Material