Inference With Misspecified Linear Mixed Effects Models

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Yang, Yaqin

Date: 

2017

Abstract: 

This thesis provides an overview of linear mixed effects models commonly used in Biostatistics for analyzing repeated measurements, which include clustered and longitudinal measurements on patients or individuals often considered in clinical studies. In this thesis, we study the properties of the maximum likelihood estimators under the assumption of skew-normal distributions for the random effects and/or random errors in linear mixed models. We find that when the "true" random effects distributions are skewed, the assumption of a skew-normal distribution for the random effects provides more robust estimators of the model parameters in terms of smaller biases and mean squared errors.

We also study the empirical levels of the likelihood ratio test for testing the significance of the skewness parameters in the skew-normal distribution. Our Simulation study suggests that the likelihood ratio test provides approximately the correct level of significance under the null hypothesis that the underlying distribution is normal.

Subject: 

Statistics

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Science: 
M.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Probability and Statistics

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).