A study of bias in the naive estimator in longitudinal linear mixed-effects models with measurement error and misclassification in covariates

Li, Jia (2014) A study of bias in the naive estimator in longitudinal linear mixed-effects models with measurement error and misclassification in covariates. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (6Mb)

Abstract

This research presents a generalized least square approach to estimate the pa- rameters in a longitudinal linear mixed-effects model. In this model, we consider measurement error and misclassification in the covariates. Moreover, a classical mea- surement error for continuous covariates, and misclassification for discrete covariates up to three categories, is considered. Through simulation studies, we observe the impact of each parameter of the model on the bias of the naive estimation, when the other parameters stay unchanged.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/8144
Item ID: 8144
Additional Information: Includes bibliographical references (pages 167-169).
Department(s): Science, Faculty of > Mathematics and Statistics
Date: July 2014
Date Type: Submission
Library of Congress Subject Heading: Longitudinal method; Linear models (Statistics); Analysis of covariance; Estimation theory; Simulation methods

Actions (login required)

View Item View Item

Downloads

Downloads per month over the past year

View more statistics