Document Type


Date of Degree

Fall 2009

Degree Name

PhD (Doctor of Philosophy)

Degree In


First Advisor

Cavanaugh, Joseph

First Committee Member

Dawson, Jeffrey D

Second Committee Member

Oleson, Jacob J

Third Committee Member

Liu, Dawei

Fourth Committee Member

Polgreen, Philip


An important challenge in statistical modeling involves determining an appropriate structural form for a model to be used in making inferences and predictions. Missing data is a very common occurrence in most research settings and can easily complicate the model selection problem. Many useful procedures have been developed to estimate parameters and standard errors in the presence of missing data;however, few methods exist for determining the actual structural form of a modelwhen the data is incomplete.

In this dissertation, we propose model selection criteria based on the Kullback-Leiber discrepancy that can be used in the presence of missing data. The criteria are developed by accounting for missing data using principles related to the expectation maximization (EM) algorithm and bootstrap methods. We formulate the criteria for three specific modeling frameworks: for the normal multivariate linear regression model, a generalized linear model, and a normal longitudinal regression model. In each framework, a simulation study is presented to investigate the performance of the criteria relative to their traditional counterparts. We consider a setting where the missingness is confined to the outcome, and also a setting where the missingness may occur in the outcome and/or the covariates. The results from the simulation studies indicate that our criteria provide better protection against underfitting than their traditional analogues.

We outline the implementation of our methodology for a general discrepancy measure. An application is presented where the proposed criteria are utilized in a study that evaluates the driving performance of individuals with Parkinson's disease under low contrast (fog) conditions in a driving simulator.


AIC, Bootstrap, EM Algorithm, Kullback-Leibler discrepancy, Missing Data, Model Selection


viii, 140 pages


Includes bibliographical references (pages 136-140).


Copyright 2009 JonDavid Sparks

Included in

Biostatistics Commons