Name: Bayesian Methods for Repeated Measures (en Inglés)
Price: 50890.00 CLP
Availability: InStock
Author: Broemeling, Lyle D.
ISBN: 9781138894044

portada Bayesian Methods for Repeated Measures (en Inglés)

Formato

Libro Físico

Autor

Lyle D. Broemeling

Editorial

CRC Press

Idioma

Inglés

N° páginas

568

Encuadernación

Tapa Blanda

ISBN13

9781138894044

N° edición

Categorías

Bibliografias
Probabilidad y estadística

Bayesian Methods for Repeated Measures (en Inglés)

Lyle D. Broemeling (Autor) · CRC Press · Tapa Blanda

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Reseña del libro "Bayesian Methods for Repeated Measures (en Inglés)"

Analyze Repeated Measures Studies Using Bayesian TechniquesGoing beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas, especially biostatistics.The author takes a practical approach to the analysis of repeated measures. He bases all the computing and analysis on the WinBUGS package, which provides readers with a platform that efficiently uses prior information. The book includes the WinBUGS code needed to implement posterior analysis and offers the code for download online. Accessible to both graduate students in statistics and consulting statisticians, the book introduces Bayesian regression techniques, preliminary concepts and techniques fundamental to the analysis of repeated measures, and the most important topic for repeated measures studies: linear models. It presents an in-depth explanation of estimating the mean profile for repeated measures studies, discusses choosing and estimating the covariance structure of the response, and expands the representation of a repeated measure to general mixed linear models. The author also explains the Bayesian analysis of categorical response data in a repeated measures study, Bayesian analysis for repeated measures when the mean profile is nonlinear, and a Bayesian approach to missing values in the response variable.