Date of Award

1-1-2013

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Epidemiology and Biostatistics

Program

Biostatistics

Content Description

1 online resource (ix, 94 pages) : color illustrations.

Dissertation/Thesis Chair

Gregory DiRienzo

Committee Members

Syni-An Hwang, Recai Yucel

Keywords

Multivariate analysis, Mathematical statistics, Multidimensional scaling, Stochastic models, Multivariate Analysis

Subject Categories

Biostatistics

Abstract

Ordered categorical responses are common in many applied studies; moreover, with the rapid growth of computational power and technologies, ultrahigh dimensional data becomes very widespread. For example, there could be ten thousands of dimensions in a gene expression data and of interest is to classify the disease stage with specific genes and predict a clinical prognosis by using these specific genes. However, there are some unique challenges, including (1) the curse of dimensionality and (2) the modeling strategy for allowing dynamic covariate effects. Thus, variable selection for mining ultrahigh dimensional data and flexible modeling strategy for allowing dynamic covariate effects in ordinal response data are urgent problems of great practical importance.

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