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.
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.
