Author

Sara Bennett

Defense Date

4-13-2004

Availability

Worldwide Access

Submission Type

thesis

Degree Name

MS

Department

Computational Mathematics

School

McAnulty College and Graduate School of Liberal Arts

Committee Chair

John C. Kern

Committee Member

Constance D. Ramirez

Committee Member

Frank D'Amico

Committee Member

Kathleen Taylor

Committee Member

Richard Pro

Keywords

Bayesian, logistic regression, MCMC

Abstract

In this research, we implement a multiple logistic regression model in which the coefficients of indicator variables are constrained to be zero or positive. By doing this, the contribution of each variable to the failure probability can be assessed. Due to this restriction on the coefficients, a Bayesian approach to parameter estimation--which assigns mixture priors to the coefficients--is taken. The data is provided by a large health insurance company in Western Pennsylvania and includes the enrollment status and corresponding values of the 84 predictor variables for 1,280,612 individuals. The insurer feels the analysis is needed to determine why its membership is declining, why its cost trend is higher than the national average, and what logical steps can be taken to reverse the current trends.

Format

PDF

Language

English

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