Details of MA4107 (Autumn 2025)
Level: 4 | Type: Theory | Credits: 4.0 |
Course Code | Course Name | Instructor(s) |
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MA4107 | Statistical Inference | Anirvan Chakraborty |
Syllabus |
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Common Families of Distributions: Location and scale families, exponential families.
Principles of Data Reduction: Introduction, sufficient statistics, minimal sufficient statistics, ancillary statistics, complete statistics, relation between ancillary and complete sufficient statistics. Point Estimation: Introduction, method of moment estimators, maximum likelihood estimators; Methods of evaluating estimators-- mean squared error, uniformly minimum variance unbiased estimator (UMVUE), sufficiency and unbiasedness, consistency and efficiency. Interval Estimation: Introduction and various methods of finding interval estimators. Hypothesis Testing: Introduction; Neyman-Pearson lemma, monotone likelihood ratio, likelihood ratio tests, union-intersection and intersection-union tests; p-values. |
Prerequisite |
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Analysis IV (MA3204) and Statistics I (MA3205) |
References |
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Suggested Texts:
1. Casella, G. and Berger, R.L., Statistical Inference, Thomson Brooks/Cole. 2. Cramer, H., Mathematical Methods of Statistics, Princeton University Press. 3. Lehman, E.L. and Casella, G., Theory of Point Estimation, Springer. 4. Lehman, E.L. and Romano, J.P., Testing Statistical Hypotheses, Springer. 5. Rao, C.R., Linear Statistical Inference and Its Applications, Wiley-Interscience. 6. Rohatgi, V.K., Statistical Inference, Dover Publications. 7. Wilks, S.S., Mathematical Statistics, Buck Press. |
Course Credit Options
Sl. No. | Programme | Semester No | Course Choice |
---|---|---|---|
1 | IP | 1 | Not Allowed |
2 | IP | 3 | Not Allowed |
3 | IP | 5 | Not Allowed |
4 | MP | 1 | Not Allowed |
5 | MP | 3 | Not Allowed |
6 | MR | 1 | Not Allowed |
7 | MR | 3 | Not Allowed |
8 | MS | 3 | Not Allowed |
9 | MS | 5 | Not Allowed |
10 | MS | 7 | Elective |
11 | MS | 9 | Elective |
12 | RS | 1 | Elective |
13 | RS | 2 | Elective |