Bayesian Local Influence for the Growth Curve Model with Rao's Simple Covariance Structure

✍ Scribed by Jian-Xin Pan; Kai-Tai Fang; Erkki P. Liski

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 916 KB
• Volume: 58
• Category: Article
• ISSN: 0047-259X
• DOI: 10.1006/jmva.1996.0039

✦ Synopsis

In this paper, the Bayesian local influence approach is employed to diagnose the adequacy of the growth curve model with Rao's simple covariance structure, based on the Kullback Leibler divergence. The Bayesian Hessian matrices of the model are investigated in detail under an abstract perturbation scheme. For illustration, covariance-weighted perturbation is considered particularly and used to analyze two real-life biological data sets, which shows that the criteria presented in this article are useful in practice.

1996 Academic Press, Inc.

1. Introduction

Since Cook (1986) gave a general method for assessing the influence of local departure from assumption in certain statistical models, this approach, based on likelihood displacement, has played increasingly important roles in statistical diagnostics. This is due to the fact that all article no.