2nd Edition
Statistical Inference An Integrated Approach, Second Edition
Introduction
Information
The concept of probability
Assessing subjective probabilities
An example
Linear algebra and probability
Notation
Outline of the book
Elements of Inference
Common statistical models
Likelihood-based functions
Bayes theorem
Exchangeability
Sufficiency and exponential family
Parameter elimination
Prior Distribution
Entirely subjective specification
Specification through functional forms
Conjugacy with the exponential family
Non-informative priors
Hierarchical priors
Estimation
Introduction to decision theory
Bayesian point estimation
Classical point estimation
Empirical Bayes estimation
Comparison of estimators
Interval estimation
Estimation in the Normal model
Approximating Methods
The general problem of inference
Optimization techniques
Asymptotic theory
Other analytical approximations
Numerical integration methods
Simulation methods
Hypothesis Testing
Introduction
Classical hypothesis testing
Bayesian hypothesis testing
Hypothesis testing and confidence intervals
Asymptotic tests
Prediction
Bayesian prediction
Classical prediction
Prediction in the Normal model
Linear prediction
Introduction to Linear Models
The linear model
Classical estimation of linear models
Bayesian estimation of linear models
Hierarchical linear models
Dynamic linear models
Linear models with constraints
Sketched Solutions to Selected Exercises
List of Distributions
References
Index
Exercises appear at the end of each chapter.
Biography
Helio S. Migon (Universidade Federal do Rio de Janeiro, Brazil) (Author) , Dani Gamerman (Universidade Federal do Rio de Janeiro, Brazil) (Author) , Francisco Louzada (Universidade Federal de Sao Carlos, Brazil) (Author)
