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Contents
Front Matter
1.
Bayesian Analysis
2.
Bayesian Analysis of One Parameter Model
3.
Bayesian Analysis of Normal Distribution - Part 1
4.
Bayesian Analysis of Normal Distribution - Part 2
5.
Bayesian Conjugate Priors for the Normal Distribution - Part 3
6.
Bayesian Analysis of Multiparamter Models
7.
Introduction to Hierarchical Models
8.
Monte Carlo Intergration and Simulation Technique - Part 1
9.
Monte Carlo Intergration and Simulation Technique - Part 2
10.
Markov Chain Monte Carlo
11.
Bayesian Regression Analysis - Part 1
12.
Bayesian Regression Analysis - Part 2
13.
More on MCMC
14.
Bayesian Hypothesis Testing and Bayes Factors
15.
Advanced Hierarchical Models - Part 1
16.
Advanced Hierarchical Models - Part 2
17.
Bayesian Generalized Linear Models
18.
Missing Data Models
19.
Nonparametric Bayesian Analysis: Dirichlet Process Models
20.
Gaussian Process Prior for Non-Parametric Regression
21.
Maximum Likelihood Estimation - The Basic Idea
22.
MLE on Truncated Parameter Space
23.
Some Features of ML Estimators
24.
Invariance Property and Likelihood Equation of MLE
25.
Multivariate Multiparameter MLE
26.
One Parameter Exponential Family and MLE
27.
Some Important Theorems on MLE
28.
MLE based on Iteration : Some Typical Examples
29.
LIKELIHOOD RATIO TEST (LRT): Basic Ideas
30.
Some Further Properties of LRT
31.
LRT : Applications under Single Distributions
32.
LRT : Applications under Two Comparable Single Distributions and Bivariate Distribution
33.
LRT : Applications under k (>2) comparable single distributions and Multivariate Distribution
34.
Some Asymptotically Equivalent Tests
35.
MLE under Survival Data: Type I and II Censoring
36.
MLE under Survival Data: Type I and Random Censoring and K-M Estimator
37.
Partial Likelihood and Cox Proportional Hazard Model
38.
Conditional and Marginal Likelihood
39.
Quasi - Likelihood Method of Estimation
Back Matter
Statistical Inference III
23
Some Features of ML Estimators
De Saurav
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