Dissertation/Thesis Abstract

Slope estimation in a parametric measurement error model: A simulation study
by Lu, Hui, M.S., The University of Texas School of Public Health, 2011, 38; 1502170
Abstract (Summary)

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.

Indexing (document details)
Advisor: Chan, Wenyaw
Commitee: Lai, Dejian, Symanski, Elaine
School: The University of Texas School of Public Health
Department: Biostatistics
School Location: United States -- Texas
Source: MAI 50/03M, Masters Abstracts International
Source Type: DISSERTATION
Subjects: Statistics, Public health
Keywords: Latent slope, Measurement error, Surrogate variable
Publication Number: 1502170
ISBN: 978-1-267-01569-3
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