Skip to content
Increase Font Size
Toggle Menu
Home
Read
Sign in
Search in book:
Search
Contents
Front Matter
Introduction
1.
Introduction to point estimation
2.
Criteria for effective estimation
3.
Unbaised Estimation
4.
Uniformly Minimum Variance Unbiased Estimator
5.
Some Results Uniformly Minimum Variance Unbiased Estimator
6.
SUFFICIENCY
7.
Fisher Neyman Factorisation
8.
Exponential families of distributions
9.
Minimal Sufficiency
10.
Completeness
11.
Complete sufficiency
12.
Ancillarity
13.
important theorems on appliacation of sufficient statistics
14.
Determination of UMVUE by Complete Sucient
15.
Cramer-Rao lower bound
16.
Bhattacharyya System of lower bound
17.
Chapman-Robbins Lower Bound
18.
Chapman-Robbins Lower Bound
19.
Cramer-Rao lower bound in case of several parameters
20.
Interval Estimation
21.
Introduction to Testing of Hypothesis
22.
Idea of Test function
23.
The Neyman-Pearson Fundamental Lemma-1
24.
The Neyman-Pearson Fundamental Lemma-2
25.
Hypothesis Testing in Uniform [0,] - I
26.
Hypothesis Testing in Uniform [0,] - II
27.
Hypothesis Testing in Uniform [0,0] - III
28.
Hypothesis testing in Shifted Exponential Population
29.
Testing of Composite Null Hypotheses against Simple Alternatives
30.
Monotone Likelihood Ratio Family -1
31.
Monotone Likelihood Ratio-2
32.
Generalized Neyman-Pearson lemma-Theory of UMPU tests
33.
Locally most powerful tests
34.
UMPU tests for multi-parameter exponential family-I
35.
UMPU tests for multi-parameter exponential family-II
36.
UMPU tests for multi-parameter exponential family-III
37.
Theory of Condence Sets
38.
Theory of Unbiased Condence Sets
Back Matter
Appendix
Statistical Inference I
11
Complete sufficiency
Shirsendu Mukherjee
