Statistical Inference: An Integrated Approach, Second Edition, 2nd Edition (Hardback) book cover

Statistical Inference

An Integrated Approach, Second Edition, 2nd Edition

By Helio S. Migon, Dani Gamerman, Francisco Louzada

Chapman and Hall/CRC

385 pages | 34 B/W Illus.

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pub: 2014-09-03
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A Balanced Treatment of Bayesian and Frequentist Inference

Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition.

New to the Second Edition

  • New material on empirical Bayes and penalized likelihoods and their impact on regression models
  • Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models
  • More examples and exercises
  • More comparison between the approaches, including their similarities and differences

Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.

Table of Contents



The concept of probability

Assessing subjective probabilities

An example

Linear algebra and probability


Outline of the book

Elements of Inference

Common statistical models

Likelihood-based functions

Bayes theorem


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


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


Classical hypothesis testing

Bayesian hypothesis testing

Hypothesis testing and confidence intervals

Asymptotic tests


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



Exercises appear at the end of each chapter.

About the Series

Chapman & Hall/CRC Texts in Statistical Science

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General