✦ LIBER ✦

Bayesian Modeling of Joint Regressions for the Mean and Covariance Matrix

✍ Scribed by Edilberto C. Cepeda; Dani Gamerman

Publisher: John Wiley and Sons
Year: 2004
Tongue: English
Weight: 306 KB
Volume: 46
Category: Article
ISSN: 0323-3847
DOI: 10.1002/bimj.200210035

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

An important problem in agronomy is the study of longitudinal data on the growth curve of the weight of cattle through time, possibly taking into account the effect of other explanatory variables such as treatments and time. In this paper, a Bayesian approach for analysing longitudinal data is proposed. It takes into account regression structures on the mean and the variance‐covariance matrix of normal observations. The approach is based on the modeling strategy suggested by Pourahmadi (1999, Biometrika 86, 667–690). After revising this methodology, we present the Bayesian approach used to fit the models, based on a generalization of the Metropolis‐Hastings algorithm of Cepeda and Gamerman (2000, Brazilian Journal of Probability and Statistics, 14, 207–221). The approach is used to the study of growth and development of a group of deaf children. The paper is concluded with a few proposed extensions. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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