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Contents
Front Matter
Introduction
1.
Introducing Nonparametric Inference
2.
Different Setups,Functions and Plug-in Estimators
3.
Kernels and Their Symmetry,U Statistic
4.
Small Sample Properties of U Statistics
5.
Large Sample Properties of U statistics
6.
Two Sample U Statistics
7.
Non-Parametrics Hypothesis Testing and Confidence Interval
8.
Sign Test I
9.
Sign Test II
10.
Wilcoxon Signed Rank Test I
11.
Goodness of Fit Tests
12.
Two Sample Procedures : Mann Whitney Test I
13.
Two Sample Procedures : Mann Whitney Test II
14.
Two Sample Procedures For Homogeneity
15.
Test for Association
16.
Linear Rank Statistics I
17.
Linear Rank Statistics II
18.
Asymptotic Relative Efficiency I(npi19)
19.
Asymptotic Relative Efficiency II
20.
Why Large Sample Methods Are Necessary
21.
Mathematical Preliminaries I
22.
Mathematical Preliminaries II
23.
Mathematical Preliminaries III
24.
Mathematical Preliminaries IV
25.
Mathematical Preliminaries V
26.
Mathematical Preliminaries VI
27.
Mathematical Preliminaries VII
28.
Mathematical Preliminaries VIII
29.
Large Sample 1
30.
Large Sample 2
31.
Large Sample 3
32.
Delta Theorems Multivariate Case
33.
Delta Theorems For Vector Valued Functions
34.
Applications of Delta Theorems
35.
Asymptotic Distribution of Sample Quanties
36.
Applications of Asymptotics Results in Interence
37.
Pearson X Tests
38.
Asymptotic Optimality
Back Matter
Appendix
Statistical Inference II
18
Asymptotic Relative Efficiency I(npi19)
Prof Rahul Bhattacharya
you can view video on Asymptotic Relative Efficiency I(npi19)
