Tools for Statistical Inference

1. Front Matter

   Pages I-VI

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2. Introduction to Problems & Techniques

   • Martin A. Tanner

   Pages 1-5

3. Observed Data Techniques-Normal Approximation

   • Martin A. Tanner

   Pages 6-15

4. Observed Data Techniques - Approximations Based on Numerical Integration, Laplace Expansions, Monte Carlo and Importance Sampling

   • Martin A. Tanner

   Pages 16-29

5. The EM Algorithm

   • Martin A. Tanner

   Pages 30-46

6. The Data Augmentation Algorithm

   • Martin A. Tanner

   Pages 47-88

7. The Gibbs Sampler

   • Martin A. Tanner

   Pages 89-107

8. Back Matter

   Pages 108-113

   PDF

From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#

• Monte Carlo method
• expectation–maximization algorithm
• generalized linear model
• latent class analysis
• likelihood
• statistical inference

• Department of Biostatistics, University of Rochester Medical Center, Rochester, USA

  Martin A. Tanner

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