Bayesian Functional Data Analysis Using WinBUGS

被引：1

作者：

Crainiceanu, Ciprian ^{[1
]}

Goldsmith, A. Jeffrey ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2010年 / 32卷 / 11期

关键词：

MCMC; mixed effects; covariance; smoothing; GENERALIZED LINEAR-MODELS; LIKELIHOOD RATIO TESTS; REGRESSION;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

引用

页码：1 / 33

页数：33

共 48 条

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