Paper Info
Title
Influence assessment in censored mixed-effects models using the multivariate Student’s-t distribution
Abstract
In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyze these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student’s-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student’s-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes.
Year
DOI
Venue
2015
10.1016/j.jmva.2015.06.014
Journal of Multivariate Analysis
Keywords
Field
DocType
62E99,62E05,62F99,62H99
Econometrics,Normality,Random effects model,Inference,Multivariate statistics,Conditional expectation,Student's t-distribution,Outlier,Statistics,Censoring (statistics),Mathematics
Journal
Volume
ISSN
Citations 
141
0047-259X
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Larissa A. Matos151.23
Dipankar Bandyopadhyay201.35
Luis M. Castro3123.51
Victor H. Lachos48511.44