Peter Congdon

Bayesian Models for Categorical Data

• Gebundenes Buch

Produktbeschreibung

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes.
_ Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data).
_ Considers missing data models techniques and non-standard models (ZIP and negative binomial).
_ Evaluates time series and spatio-temporal models for discrete data.
_ Features discussion of univariate and multivariate techniques.
_ Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site.
The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 448
• Erscheinungstermin: 11. Juli 2005
• Englisch
• Abmessung: 250mm x 175mm x 29mm
• Gewicht: 975g
• ISBN-13: 9780470092378
• ISBN-10: 0470092378
• Artikelnr.: 14082677

Autorenporträt

Peter Congdon, Queen Mary, University of London, UK Peter is the author of two best-selling Wiley books on Bayesian modelling - Bayesian Statistical Modelling, and Applied Bayesian Modelling.

Inhaltsangabe

Chapter 1: Principles of Bayesian Inference. Chapter 2: Model Comparison
and Choice. Chapter 3: Regression for Metric Outcomes. Chapter 4: Models
for Binary and Count Outcomes. Chapter 5: Further Questions in Binomial and
Count Regression. Chapter 6: Random Effect and Latent Variable Models for
Multicategory Outcomes. Chapter 7: Ordinal Regression. Chapter 8: Discrete
Spatial Data. Chapter 9: Time Series Models for Discrete Variables. Chapter
10: Hierarchical and Panel Data Models Chapter 11: Missing-Data Models.
Index.

Rezensionen

" a good book on the shelves of researchers in categorical data analysis." ( Technometrics , May 2007) " valuable for anyone interested in how Bayesian ideas apply in practice an should prove useful for anyone using the WINBUGS package for categorical data analysis." ( Biometrics , March 2007) " an excellent resource for biostatisticians and medical researchers." ( Doody s Health Services ) " perfectly suited as a reference for any practitioner .Congdon has done a laudable job of introducing jointly the concepts of categorical data and Bayesian analysis." ( Journal of the American Statistical Association , June 2006) "The author s clear and logical approach makes the book accessible" ( Zentralblatt MATH Volume 1079)