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CRC Press Generalized Linear Models: Bayesian Perspective - Statistics

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Description

Generalized Linear Models: A Bayesian Perspective offers a comprehensive guide to understanding and applying GLMs through Bayesian computational methods and simulation-based inference. This Chapman & Hall/CRC Biostatistics Series volume equips statisticians and researchers with both parametric and semiparametric approaches to overdispersed GLMs, making it essential for anyone working with complex statistical modelling. The book introduces dynamic modelling techniques for GLMs and covers methods for analysing correlated binary data using latent variables, whilst proposing innovative semiparametric approaches to model link functions for binary response data. With over 1000 references and equations throughout, this text bridges traditional frequentist GLM theory and modern Bayesian computational practice, providing rigorous mathematical foundations alongside practical implementation guidance for contemporary statistical analysis.

Key Features

Covers parametric and semiparametric approaches to overdispersed generalized linear models for handling complex data structures.

Presents methods for analysing correlated binary data using latent variables and advanced Bayesian techniques.

Introduces dynamic modelling for generalized linear models with modern computational methods for simulation-based inference.

Contains over 1000 references and equations supporting comprehensive understanding of Bayesian GLM theory and application.

Proposes semiparametric methods to model link functions for binary response data in practical statistical modelling scenarios.

Part of Chapman & Hall/CRC Biostatistics Series, combining rigorous theory with applied statistical methodology.

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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