Statistical Inference

This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference. Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.

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.

  Autorentext
  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)

  Zusammenfassung

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.

  Inhalt

Introduction. Elements of Inference. Prior Distribution. Estimation. Approximating Methods. Hypothesis Testing. Prediction. Introduction to Linear Models. Sketched Solutions to Selected Exercises. List of Distributions. References. Index.