Bayesian influence analysis of generalized partial linear mixed models for longitudinal data

Nian-Sheng Tang and Xing-De Duan

Journal of Multivariate Analysis, 2014, vol. 126, issue C, 86-99

Abstract: This paper develops a Bayesian local influence approach to assess the effects of minor perturbations to the prior, sampling distribution and individual observations on the statistical inference in generalized partial linear mixed models (GPLMMs) with the distribution of random effects specified by a truncated and centered Dirichlet process (TCDP) prior. A perturbation manifold is defined. The metric tensor is employed to select an appropriate perturbation vector. Several Bayesian local influence measures are proposed to quantify the degree of various perturbations to statistical models based on the first and second-order approximations to the objective functions including the ϕ-divergence, the posterior mean distance and Bayes factor. We develop two Bayesian case influence measures to detect the influential observations in GPLMMs based on the ϕ-divergence and Cook’s posterior mean distance. The computationally feasible formulae for Bayesian influence analysis are given. Several simulation studies and a real example are presented to illustrate the proposed methodologies.

Keywords: Bayesian case influence; Bayesian local influence; Bayesian perturbation manifold; Dirichlet process prior; Generalized partial linear mixed models (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13002650
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:126:y:2014:i:c:p:86-99

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.12.005

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().