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.

引用

页码：2473 / 2484

页数：12

共 35 条

[1]

[Anonymous], 2003, Semiparametric Regression

[2]

Berger J.O., 1992, BAYESIAN ANAL STAT E, P323

[3]

Berger J. O., 1992, Bayesian Statistics, V4, P35

[4] Objective Bayesian analysis of spatially correlated data [J].