SYLLABUS

Channel 29: UGC-INFLIBNET (PG Subject's & YOGA)
Course : Statistical Inference I , Target USER : PG
No Title Duration Author Author's Affiliation Language Video
1 M-01. Introduction to point estimation 0:21:48 Prof Siddhartha Nandy University of Michigan English
2 M-02. Criteria for effective estimation 00:19:46 Prof Siddhartha Nandy University of Michigan English
3 M-03. Unbiased estimation 0:20:26 Prof Siddhartha Nandy University of Michigan English
4 M-04. Uniformly minimum variance unbiased estimators 0:23:08 Prof Siddhartha Nandy University of Michigan English
5 M-05. Some results on uniformly minimum variance unbiased estimators 0:24:01 Prof Siddhartha Nandy University of Michigan English
6 M-06. Sufficiency 0:27:10 Prof Siddhartha Nandy University of Michigan English
7 M-07. Fisher Neyman factorisation 0:30:07 Prof Siddhartha Nandy University of Michigan English
8 M-08. Exponential family of distributions 0:20:27 Prof Siddhartha Nandy University of Michigan English
9 M-09. Minimal Sufficiency 0:28:21 Prof Siddhartha Nandy University of Michigan English
10 M-10. Completeness 0:23:19 Prof Siddhartha Nandy University of Michigan English
11 M-11. Complete sufficiency 0:22:12 Prof Siddhartha Nandy University of Michigan English
12 M-12. Ancillarity 0:22:42 Prof Siddhartha Nandy University of Michigan English
13 M-13. Important theorems on application of sufficient statistics 0:20:44 Prof Siddhartha Nandy University of Michigan English
14 M-14. Determination of UMVUE through complete sufficient statistics 0:22:28 Prof Siddhartha Nandy University of Michigan English
15 M-15. Fisher's information function 0:23:06 Prof Siddhartha Nandy University of Michigan English
16 M-16. Bhattacharya system of lower bounds 0:19:41 Prof Siddhartha Nandy University of Michigan English
17 M-17. Chapman-Robbins lower bound 0:24:19 Prof Siddhartha Nandy University of Michigan English
18 M-18. Cramer Rao lower bound 0:20:05 Prof Siddhartha Nandy University of Michigan English
19 M-19. Cramer-Rao lower bound in case of several parameters 0:26:59 English
20 M-20. Introduction to interval testing 0:32:19 Prof Siddhartha Nandy University of Michigan English
21 M-21. Introduction to Testing of Hypothesis 0:25:54 Prof Siddhartha Nandy University of Michigan English
22 M-22. Idea of a test function 0:28:04 Prof Siddhartha Nandy University of Michigan English
23 M-23. The Neyman Pearson fundamental lemma- I 0:16:17 Prof Siddhartha Nandy University of Michigan English
24 M-24. The Neyman Pearson fundamental lemma -II 0:12:49 Prof Siddhartha Nandy University of Michigan English
25 M-25. Hypothesis Testing in Uniform [0,θ] - I 0:21:14 English
26 M-25. Hypothesis Testing in Uniform [0,θ] - I 0:21:15 Prof Siddhartha Nandy University of Michigan English
27 M-26. Hypothesis Testing in Uniform [0,θ] - II 0:16:09 English
28 M-27. Hypothesis Testing in Uniform [0,θ] - III 0:18:32 English
29 M-28. Hypothesis test for shifted exponential 0:14:09 Prof Siddhartha Nandy University of Michigan English
30 M-29. Testing of Composite Null Hypotheses against Simple Alternatives 0:22:30 English
31 M-30. Monotone likelihood ratio family 1 0:21:12 English
32 M-31. Monotone likelihood ratio 2 0:23:10 English
33 M-32. Generalised Neyman Pearson theorem: UMPU tests 0:23:41 English
34 M-33. Locally most powerful tests 0:24:14 English
35 M-34. UMPU tests for multi-parameter exponential family-I 0:21:28 English
36 M-35. UMPU tests for multi-parameter exponential family-II 0:22:49 Prof Siddhartha Nandy University of Michigan English
37 M-36. UMPU tests for multi-parameter exponential family-III 0:18:49 Prof Siddhartha Nandy University of Michigan English
38 M-37. Theory of Confidence Sets 0:23:06 Prof Siddhartha Nandy University of Michigan English
39 M-38. Theory of Unbiased Confidence Sets 0:22:19 Prof Siddhartha Nandy University of Michigan English

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