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SYLLABUS

Class-7 PMeVIDYA

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