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.
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.
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 |