Mixed-effects Tobit joint models for longitudinal data with skewness, detection limits, and measurement errors
type: Item
instance of: scholarly article
title: Mixed-effects Tobit joint models for longitudinal data with skewness, detection limits, and measurement errors
zbMATH Open document ID: 1233.62042
DOI: 10.1155/2012/614102
author: Getachew A. Dagne
author: Yangxin Huang
published in: Journal of Probability and Statistics
publication date: 2012-03-13T00:00:00Z
review text: Summary: Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. We jointly investigate a skew-t NLME Tobit model for response (with left censoring) process and a skew-t nonparametric mixed-effects model for a covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62H10
Mathematics Subject Classification ID: 62N02
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6014386
zbMATH Keywords: AIDS clinical trial
Wikidata QID: Q58910976
describes a project that uses: MEMSS
describes a project that uses: WinBUGS
describes a project that uses: S-PLUS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/614102
OpenAlex ID: W2012532075
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description: scientific article; zbMATH DE number 6014386