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Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics)

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Synopses & Reviews

Publisher Comments:

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.

Book News Annotation:

Using Bayesian methods to analyze data has become common in applied statistics, social sciences, and medicine, along with other disciplines requiring close work with a diverse set of data. In this undergraduate text, Congdon (Queen Mary College, U. of London) takes a practical and accessible approach, focusing on statistical computing and applied data as he covers the principles of Bayesian inference, model comparison and choice, regression for metric outcomes, models for binary and count outcomes, random effect and latent variable models for multi-category outcomes, ordinal regression, discrete spatial data, time series models for discrete variables, hierarchical and panel data models and missing-data models.
Annotation 2005 Book News, Inc., Portland, OR (booknews.com)

Book News Annotation:

Using Bayesian methods to analyze data has become common in applied statistics, social sciences, and medicine, along with other disciplines requiring close work with a diverse set of data. In this undergraduate text, Congdon (Queen Mary College, U. of London) takes a practical and accessible approach, focusing on statistical computing and applied data as he covers the principles of Bayesian inference, model comparison and choice, regression for metric outcomes, models for binary and count outcomes, random effect and latent variable models for multi-category outcomes, ordinal regression, discrete spatial data, time series models for discrete variables, hierarchical and panel data models and missing-data models. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

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.

Synopsis:

Categorical, or discrete, data is one of the most common types of data available. Bayesian methods are increasingly being used for the modeling of such data, yet there is no book available that provides an overview of Bayesian models for analyzing categorical data. Bayesian Models for Categorical Data provides such an overview, together with a huge number of worked examples to illustrate the methods. WinBUGS code for the examples will be made available via ftp, so that the reader can apply the methods to their own data. The book also includes exercises that require the student to do further analysis of the included examples, and enabling use of the book as a course text. The author has published two very successful books on Bayesian modeling, and this book complements those with additional material, particularly on the topic of missing data, and by focusing on modeling of categorical data.

About the Author

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.

Table of Contents

Preface.

Chapter 1 Principles of Bayesian Inference.

1.1 Bayesian updating.

1.2 MCMC techniques.

1.3 The basis for MCMC.

1.4 MCMC sampling algorithms.

1.5 MCMC convergence.

1.6 Competing models.

1.7 Setting priors.

1.8 The normal linear model and generalized linear models.

1.9 Data augmentation.

1.10 Identifiability.

1.11 Robustness and sensitivity.

1.12 Chapter themes.

References.

Chapter 2 Model Comparison and Choice.

2.1 Introduction: formal methods, predictive methods and penalized deviance criteria.

2.2 Formal Bayes model choice.

2.3 Marginal likelihood and Bayes factor approximations.

2.4 Predictive model choice and checking.

2.5 Posterior predictive checks.

2.6 Out-of-sample cross-validation.

2.7 Penalized deviances from a Bayes perspective.

2.8 Multimodel perspectives via parallel sampling.

2.9 Model probability estimates from parallel sampling.

2.10 Worked example.

References.

Chapter 3 Regression for Metric Outcomes.

3.1 Introduction: priors for the linear regression model.

3.2 Regression model choice and averaging based on predictor selection.

3.3 Robust regression methods: models for outliers.

3.4 Robust regression methods: models for skewness and heteroscedasticity.

3.5 Robustness via discrete mixture models.

3.6 Non-linear regression effects via splines and other basis functions.

3.7 Dynamic linear models and their application in non-parametric regression.

Exercises.

References.

Chapter 4; Models for Binary and Count Outcomes.

4.1 Introduction: discrete model likelihoods vs. data augmentation.

4.2 Estimation by data augmentation: the Albert–Chib method.

4.3 Model assessment: outlier detection and model checks.

4.4 Predictor selection in binary and count regression.

4.5 Contingency tables.

4.6 Semi-parametric and general additive models for binomial and count responses.

Exercises.

References.

Chapter 5 Further Questions in Binomial and Count Regression.

5.1 Generalizing the Poisson and binomial: overdispersion and robustness.

5.2 Continuous mixture models.

5.3 Discrete mixtures.

5.4 Hurdle and zero-inflated models.

5.5 Modelling the link function.

5.6 Multivariate outcomes.

Exercises.

References.

Chapter 6 Random Effect and Latent Variable Models for Multicategory Outcomes.

6.1 Multicategory data: level of observation and relations between categories.

6.2 Multinomial models for individual data: modelling choices.

6.3 Multinomial models for aggregated data: modelling contingency tables.

6.4 The multinomial probit.

6.5 Non-linear predictor effects.

6.6 Heterogeneity via the mixed logit.

6.7 Aggregate multicategory data: the multinomial–Dirichlet model and extensions.

6.8 Multinomial extra variation.

6.9 Latent class analysis.

Exercises.

References.

Chapter 7 Ordinal Regression.

7.1 Aspects and assumptions of ordinal data models.

7.2 Latent scale and data augmentation.

7.3 Assessing model assumptions: non-parametric ordinal regression and assessing ordinality.

7.4 Location-scale ordinal regression.

7.5 Structural interpretations with aggregated ordinal data.

7.6 Log-linear models for contingency tables with ordered categories.

7.7 Multivariate ordered outcomes.

Exercises.

References.

Chapter 8Discrete Spatial Data.

8.1 Introduction.

8.2 Univariate responses: the mixed ICAR model and extensions.

8.3 Spatial robustness.

8.4 Multivariate spatial priors.

8.5 Varying predictor effect models.

Exercises.

References.

Chapter 9 Time Series Models for Discrete Variables.

9.1 Introduction: time dependence in observations and latent data.

9.2 Observation-driven dependence.

9.3 Parameter-driven dependence via DLMs.

9.4 Parameter-driven dependence via autocorrelated error models.

9.5 Integer autoregressive models.

9.6 Hidden Markov models.

Exercises.

References.

Chapter 10 Hierarchical and Panel Data Models

10.1 Introduction: clustered data and general linear mixed models.

10.2 Hierarchical models for metric outcomes.

10.3 Hierarchical generalized linear models.

10.4 Random effects for crossed factors.

10.5 The general linear mixed model for panel data.

10.6 Conjugate panel models.

10.7 Growth curve analysis.

10.8 Multivariate panel data.

10.9 Robustness in panel and clustered data analysis.

10.10 APC and spatio-temporal models.

10.11 Space–time and spatial APC models.

Exercises.

References.

Chapter 11 Missing-Data Models.

11.1 Introduction: types of missing data.

11.2 Density mechanisms for missing data.

11.3 Auxiliary variables.

11.4 Predictors with missing values.

11.5 Multiple imputation.

11.6 Several responses with missing values.

11.7 Non-ignorable non-response models for survey tabulations.

11.8 Recent developments.

Exercises.

References.

Index.

Product Details

ISBN:
9780470092378
Author:
Congdon, Peter
Publisher:
John Wiley & Sons
Author:
Professor Peter Congdon
Subject:
Multivariate analysis
Subject:
Bayesian statistical decision theory
Subject:
Probability & Statistics - Bayesian Analysis
Subject:
Bayesian analysis
Subject:
Mathematics - General
Copyright:
Edition Description:
WOL online Book (not BRO)
Series:
Wiley Series in Probability and Statistics
Series Volume:
626
Publication Date:
January 2005
Binding:
HARDCOVER
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
Y
Pages:
446
Dimensions:
244 x 168 x 31 mm 35 oz

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Related Subjects

Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics) New Hardcover
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$140.25 Backorder
Product details 446 pages John Wiley & Sons - English 9780470092378 Reviews:
"Synopsis" by , 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.

"Synopsis" by , Categorical, or discrete, data is one of the most common types of data available. Bayesian methods are increasingly being used for the modeling of such data, yet there is no book available that provides an overview of Bayesian models for analyzing categorical data. Bayesian Models for Categorical Data provides such an overview, together with a huge number of worked examples to illustrate the methods. WinBUGS code for the examples will be made available via ftp, so that the reader can apply the methods to their own data. The book also includes exercises that require the student to do further analysis of the included examples, and enabling use of the book as a course text. The author has published two very successful books on Bayesian modeling, and this book complements those with additional material, particularly on the topic of missing data, and by focusing on modeling of categorical data.
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