Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway

Bayesian Regression Modeling with INLA (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 324
• Erscheinungstermin: 29. Januar 2018
• Englisch
• ISBN-13: 9781351165747
• Artikelnr.: 54170126

Autorenporträt

Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.

Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.

Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

Inhaltsangabe

Introduction to Bayesian Statistics. Bayesian Hierarchical Modeling.
Model-Based Bayesian Inference. Linear and Generalized Linear Models.
Linear and Generalized Linear Mixed Models. Zero-Inflated Mixture Models.
Survival Analysis. Nonparametric Regression and Additive Models. Functional
Regression Models. Measurement Error Models. Quantile Regression.