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Bayesian Models for Categorical Data
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Artikel-Nr:
9780470092378
Veröffentl:
2005
Erscheinungsdatum:
11.07.2005
Seiten:
448
Autor:
Peter Congdon
Gewicht:
949 g
Format:
250x175x29 mm
Sprache:
Englisch
Beschreibung:
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 .
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.
* 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.
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.
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.
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.
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