Bayesian local influence for the growth curve model with Rao's simple covariance structure

Jian Xin Pan*, Kai Tai Fang, Erkki P. Liski

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)55-81
Number of pages27
JournalJournal of Multivariate Analysis
Volume58
Issue number1
DOIs
Publication statusPublished - Jul 1996

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Bayesian hessian matrix
  • Bayesian local influence
  • Covariance weighted perturbation
  • Growth curve model
  • Kullback-Leibler divergence
  • Statistical diagnostics

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