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Bayesian Methods for Data Analysis

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ISBN-10: 1584886978

ISBN-13: 9781584886976

Edition: 3rd 2008 (Revised)

Authors: Bradley P. Carlin, Thomas A. Louis

List price: $94.95
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Description:

The third edition of Bayesian Methods for Data Analysis has been updated to provide a more accessible introduction to the foundations of Bayesian analysis along with a stronger focus on applications, including case studies in biostatistics, epidemiology, and genetics. This edition features a new chapter on Bayesian design that presents Bayesian clinical trials and special topics such as missing data and causality. With an emphasis on computation, there is also expanded coverage of WinBUGS, R, and BRugs. The book contains additional exercises and solutions for undergraduate students, graduate students, and researchers in statistics and biostatistics.
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Book details

List price: $94.95
Edition: 3rd
Copyright year: 2008
Publisher: CRC Press LLC
Publication date: 6/30/2008
Binding: Hardcover
Pages: 552
Size: 6.50" wide x 9.50" long x 1.50" tall
Weight: 2.156

Donald A. Berry, The University of Texas M.D. Anderson Cancer Center, Houston, USAScott M. Berry, Berry Consultants, College Station, Texas, USABradley P. Carlin, University of Minnesota, Minneapolis, USAJ. Jack Lee, The University of Texas M.D. Anderson Cancer Center, Houston, USAPeter Muller, The University of Texas M.D. Anderson Cancer Center, Houston, USA

Preface to the Third Edition
Approaches for statistical inference
Introduction
Motivating vignettes
Personal probability
Missing data
Bioassay
Attenuation adjustment
Defining the approaches
The Bayes-frequentist controversy
Some basic Bayesian models
A Gaussian/Gaussian (normal/normal) model
A beta/binomial model
Exercises
The Bayes approach
Introduction
Prior distributions
Elicited priors
Conjugate priors
Noninformative priors
Other prior construction methods
Bayesian inference
Point estimation
Interval estimation
Hypothesis testing and Bayes factors
Hierarchical modeling
Normal linear models
Effective model size and the DIC criterion
Model assessment
Diagnostic measures
Model averaging
Nonparametric methods
Exercises
Bayesian computation
Introduction
Asymptotic methods
Normal approximation
Laplace's method
Noniterative Monte Carlo methods
Direct sampling
Indirect methods
Markov chain Monte Carlo methods
Gibbs sampler
Metropolis-Hastings algorithm
Slice sampler
Hybrid forms, adaptive MCMC, and other algorithms
Variance estimation
Convergence monitoring and diagnosis
Exercises
Model criticism and selection
Bayesian modeling
Linear models
Nonlinear models
Binary data models
Bayesian robustness
Sensitivity analysis
Prior partitioning
Model assessment
Bayes factors via marginal density estimation
Direct methods
Using Gibbs sampler output
Using Metropolis-Hastings output
Bayes factors via sampling over the model space
Product space search
"Metropolized" product space search
Reversible jump MCMC
Using partial analytic structure
Other model selection methods
Penalized likelihood criteria: AIC, BIC, and DIC
Predictive model selection
Exercises
The empirical Bayes approach
Introduction
Parametric EB (PEB) point estimation
Gaussian/Gaussian models
Computation via the EM algorithm
EB performance of the PEB
Stein estimation
Nonparametric EB (NPEB) point estimation
Compound sampling models
Simple NPEB (Robbins' method)
Interval estimation
Morris' approach
Marginal posterior approach
Bias correction approach
Bayesian processing and performance
Univariate stretching with a two-point prior
Multivariate Gaussian model
Frequentist performance
Gaussian/Gaussian model
Beta/binomial model
Empirical Bayes performance
Point estimation
Interval estimation
Exercises
Bayesian design
Principles of design
Bayesian design for frequentist analysis
Bayesian design for Bayesian analysis
Bayesian clinical trial design
Classical versus Bayesian trial design
Bayesian assurance
Bayesian indifference zone methods
Other Bayesian approaches
Extensions
Applications in drug and medical device trials
Binary endpoint drug trial
Cox regression device trial with interim analysis
Exercises
Special methods and models
Estimating histograms and ranks
Bayesian ranking
Histogram and triple goal estimates
Robust prior distributions
Order restricted inference
Longitudinal data models
Continuous and categorical time series
Survival analysis and frailty models
Statistical models
Treatment effect prior determination
Computation and advanced models
Sequential analysis
Model and loss structure
Backward induction
Forward sampling
Spatial and spatio-temporal models
Point source data models
Regional summary data models
Exercises
Case studies
Analysis of longitudinal AIDS data
Introduction and background
Modeling of longitudinal CD4 counts
CD4 response to treatment at two months
Survival analysis
Discussion
Robust analysis of clinical trials
Clinical background
Interim monitoring
Prior robustness and prior scoping
Sequential decision analysis
Discussion
Modeling of infectious diseases
Introduction and data
Stochastic compartmental model
Parameter estimation and model building
Results
Discussion
Appendices
Distributional catalog
Discrete
Univariate
Multivariate
Continuous
Univariate
Multivariate
Decision theory
Introduction
Risk and admissibility
Unbiased rules
Bayes rules
Minimax rules
Procedure evaluation and other unifying concepts
Mean squared error (MSE)
The variance-bias tradeoff
Other loss functions
Generalized absolute loss
Testing with a distance penalty
A threshold loss function
Multiplicity
Multiple testing
Additive loss
Non-additive loss
Exercises
Answers to selected exercises
References
Author index
Subject index
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