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## Details

Additional Physical Format: | ebook version : |
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Document Type: | Book |

All Authors / Contributors: |
Xiaofeng Wang, (Professor of medicine); Yu Yue; Julian James Faraway |

ISBN: | 9781498727259 1498727255 |

OCLC Number: | 1008765295 |

Notes: | Includes index. |

Description: | xii, 312 pages ; 24 cm |

Contents: | 1.Introduction Quick Start Hubble's Law Standard Analysis Bayesian Analysis INLA Bayes Theory Prior and Posterior Distributions Model Checking Model Selection Hypothesis testing Bayesian Computation Exact Sampling Approximation 2.Theory of INLA Latent Gaussian Models (LGMs) Gaussian Markov Random Fields (GMRFs) Laplace Approximation and INLA INLA Problems Extensions 3.Bayesian Linear Regression Introduction Bayesian Inference for Linear Regression Prediction Model Selection and Checking Model Selection by DIC Posterior Predictive Model Checking Cross-validation Model Checking Bayesian Residual Analysis Robust Regression Analysis of Variance Ridge Regression for Multicollinearity Regression with Autoregressive Errors 4.Generalized Linear Models GLMs Binary Responses Count Responses Poisson Regression Negative binomial regression Modeling Rates Gamma Regression for Skewed Data Proportional Responses Modeling Zero-inflated Data 5.Linear Mixed and Generalized Linear Mixed Models Linear Mixed Models Single Random Effect Choice of Priors Random Effects Longitudinal Data Random Intercept Random Slope and Intercept Prediction Classical Z-matrix Model Ridge Regression Revisited Generalized Linear Mixed Models Poisson GLMM Binary GLMM Improving the Approximation 6.Survival Analysis Introduction Semiparametric Models Piecewise Constant Baseline Hazard Models Stratified Proportional Hazards Models Accelerated Failure Time Models Model Diagnosis Interval Censored Data Frailty Models Joint Modeling of Longitudinal and Time-to-event Data 7.Random Walk Models for Smoothing Methods Introduction Smoothing Splines Random Walk (RW) Priors for Equally-spaced Locations Choice of Priors on s e and sf Random Walk Models for Non-equally Spaced Locations Thin-plate Splines Thin-plate Splines on Regular Lattices Thin-plate Splines at Irregularly-spaced Locations Besag Spatial Model Penalized Regression Splines (P-splines) Adaptive Spline Smoothing Generalized Nonparametric Regression Models Excursion Set with Uncertainty 8.Gaussian Process Regression Introduction Penalized Complexity Priors Credible Bands for Smoothness Non-stationary Fields Interpolation with Uncertainty Survival Response 9.Additive and Generalized Additive Models Additive Models Generalized Additive Models Binary response Count response Generalized Additive Mixed Models 10.Errors-in-Variables Regression Introduction Classical Errors-in-Variables Models A simple linear model with heteroscedastic errors-invariables A general exposure model with replicated measurements Berkson Errors-in-Variables Models 11.Miscellaneous Topics in INLA Splines as a Mixed Model Truncated Power Basis Splines O'Sullivan Splines Example: Canadian Income Data Analysis of Variance for Functional Data Extreme Values Density Estimation using INLA Appendix A Installation Appendix B Uninformative Priors in Linear Regression Index |

Series Title: | Chapman & Hall/CRC computer science and data analysis series |

Other Titles: | Bayesian regression modeling with integrated Laplace approximation |

Responsibility: | Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

"The book focuses on regression models with R-INLA and it will be of interest to a wide audience. INLA is becoming a very popular method for approximate Bayesian inference and it is being applied to many problems in many different fields. This book will be of interest not only to statisticians but also to applied researchers in other disciplines interested in Bayesian inference. This book can probably be used as a reference book for research and as a textbook at graduate level."~Virgilio Gomez-Rubio, University of Castilla-La Mancha"This is a well-written book on an important subject, for which there is a lack of good introductory material. The tutorial-style works nicely, and they have an excellent set of examples. They manage to do a practical introduction with just the right amount of theory background...The book should be very useful to scientists who want to analyze data using regression models. INLA allows users to fit Bayesian models quickly and without too much programming effort, and it has been used successfully in many applications. The book is written in a tutorial style, while explaining the basics of the needed theory very well, so it could serve both as a reference or textbook...The book is well written and technically correct." ~Egil Ferkingstad, deCode genetics"The authors have done a great job of not over-doing the technical details, thereby making the presentation accessible to a broader audience beyond the statistics world...It covers many contemporary parametric, nonparametric, and semiparametric methods that applied scientists from many fields use in modern research."~Adam Branscum, Oregon State University Read more...

*User-contributed reviews*