Date on Master's Thesis/Doctoral Dissertation

5-2015

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Bioinformatics and Biostatistics

Degree Program

Biostatistics with a specialization in Decision Science, PhD

Committee Chair

Datta, Somnath

Committee Co-Chair (if applicable)

Datta, Susmita

Committee Member

Sunkara, Mahendra

Committee Member

Kulasekera, KB

Subject

Mathematical statistics; Medicine--Research--Statistical methods; Materials science--Research

Abstract

In this work we present three topics, each of which centered on either the application or modification of various linear regression methods. Our work with respect to the “Materials Genome” project while undermined by oversimplification and data integrity issues in its early stages, provides a sound platform from which the project can proceed successfully. Building upon a growing body of knowledge around the use of Weighted Generalized Estimating Equations (WGEE), our second investigation proposes an extension to that framework intended to address the inherent bias present in the analysis of clustered longitudinal data with potentially informative cluster sizes and temporal observation profiles. Having demonstrated the utility of our marginal WGEE’s with respect to mitigating induced bias our final investigation presents a comparison of our marginal WGEE’s to model estimation via Joint Likelihood maximization in certain simulation models. We find, as would be in line with expectation, comparable performance with a loss of efficiency in the marginal WGEE setting.

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