Carlin / Louis Bayesian Methods for Data Analysis, Third Edition

3. Auflage 2011
ISBN: 978-1-58488-698-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 552 Seiten

Reihe: Chapman & Hall/CRC Texts in Statistical Science

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.
New to the Third Edition
- New data examples, corresponding R and WinBUGS code, and homework problems

- Explicit descriptions and illustrations of hierarchical modeling—now commonplace in Bayesian data analysis

- A new chapter on Bayesian design that emphasizes Bayesian clinical trials

- A completely revised and expanded section on ranking and histogram estimation

- A new case study on infectious disease modeling and the 1918 flu epidemic

- A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem—available both electronically and in print

Ideal for Anyone Performing Statistical Analyses
Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

Zielgruppe

Graduate and advanced undergraduate students in statistics and biostatistics; researchers in statistics, biostatistics, computer science, environmental science, engineering, medicine, and the social sciences.

Autoren/Hrsg.

Carlin, Bradley P.

Louis, Thomas A.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Approaches for statistical inference
Introduction
Motivating Vignettes
Defining the Approaches
The Bayes-Frequentist Controversy
Some Basic Bayesian Models
The Bayes approach
Introduction
Prior Distributions
Bayesian Inference
Hierarchical Modeling
Model Assessment
Nonparametric Methods
Bayesian computation
Introduction
Asymptotic Methods
Noniterative Monte Carlo Methods
Markov Chain Monte Carlo Methods
Model criticism and selection
Bayesian Modeling
Bayesian Robustness
Model Assessment
Bayes Factors via Marginal Density Estimation
Bayes Factors via Sampling over the Model Space
Other Model Selection Methods
The empirical Bayes approach
Introduction
Parametric EB Point Estimation
Nonparametric EB Point Estimation
Interval Estimation
Bayesian Processing and Performance
Frequentist Performance
Empirical Bayes Performance
Bayesian design
Principles of Design
Bayesian Clinical Trial Design
Applications in Drug and Medical Device Trials
Special methods and models
Estimating Histograms and Ranks
Order Restricted Inference
Longitudinal Data Models
Continuous and Categorical Time Series
Survival Analysis and Frailty Models
Sequential Analysis
Spatial and Spatio-Temporal Models
Case studies
Analysis of Longitudinal AIDS Data
Robust Analysis of Clinical Trials
Modeling of Infectious Diseases
Appendices
Distributional Catalog
Decision Theory
Answers to Selected Exercises
References
Author Index
Subject Index
Index
Exercises appear at the end of each chapter.