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CRC Press Generalized Linear Models: A Bayesian Perspective

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Description

Master complex statistical concepts with Generalized Linear Models: A Bayesian Perspective from the Chapman & Hall/CRC Biostatistics Series. This comprehensive volume provides a detailed guide on how to conceptualize, perform, and critique traditional generalized linear models (GLMs) through a Bayesian lens. By integrating modern computational methods, readers learn to summarize inferences using simulation techniques. This text covers essential topics for advanced practitioners, including parametric and semiparametric approaches to overdispersed GLMs. It offers practical methods for analyzing correlated binary data using latent variables and introduces a semiparametric method to model link functions for binary response data. With the addition of dynamic modeling for GLMs, this book serves as a deep resource for those looking to expand their statistical toolkit. Whether you are researching or applying these models in professional practice, this volume provides the mathematical foundation and modern computational strategies needed to navigate complex data sets.

Key Features

Learn to conceptualize and critique traditional generalized linear models using a Bayesian perspective for more robust statistical analysis.

Utilize modern computational methods to effectively summarize inferences through simulation techniques.

Explore dynamic modeling for GLMs alongside parametric and semiparametric approaches to overdispersed models.

Gain methods for analyzing correlated binary data through the application of latent variables.

Access a massive technical resource containing over 1000 references and equations for deep study.

Implement semiparametric methods specifically designed to model link functions for binary response data.

Product Specifications

Format
Hardcover
Domain
Amazon UK
Release Date
25 May 2000
Listed Since
27 December 2006

Barcode

No barcode data available

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