Date of Award

7-6-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Department of Mathematics

First Advisor

Pei Geng

Abstract

In a logistic regression model, when the covariate is measured with error, the estimators of the regression coefficient parameters can be biased. We propose a method for estimating parameters of a logistic regression with case-control data, when the covariate is subject to measurement error. The density of the covariate is estimated by using the deconvolution kernel density estimation. The parameters of the regression are estimated by the integrated squared distance based on the log ratio of the estimated density. We show the consistency and the asymptotic normality of the proposed estimators. Simulation study shows the superiority of the proposed method in different sample sizes and measurement error magnitudes scenario. The methodology is applied to estimating the relationship of systolic blood pressure and the presence of coronary heart disease.

Comments

Imported from Nguyen_ilstu_0092N_11990.pdf

DOI

https://doi.org/10.30707/ETD2021.20211012065805038957.999968

Page Count

63

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