Reference priors for linear models with general covariance structures

被引：1
|
作者：
Zhao, Xin [1 ]
Wells, Martin T. [2 ]
机构：
[1] Merck Res Labs, N Wales, PA 19454 USA
[2] Cornell Univ, Dept Stat Sci, Ithaca, NY 14853 USA
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 08期
关键词：
Covariance estimation; General linear models; Graphical models; Linear mixed models; MCMC; Penalized splines; Reference prior; Smoothing; Structural equation models; DISTRIBUTIONS; SELECTION; MATRIX;
D O I：
10.1016/j.jspi.2012.01.005
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We develop a new class of reference priors for linear models with general covariance structures. A general Markov chain Monte Carlo algorithm is also proposed for implementing the computation. We present several examples to demonstrate the results: Bayesian penalized spline smoothing, a Bayesian approach to bivariate smoothing for a spatial model, and prior specification for structural equation models. (C) 2012 Published by Elsevier B.V.
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页码：2473 / 2484
页数：12
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