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Activity Number: 120 - SPEED: Nonparametric Statistics: Estimation, Testing, and Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #329391
Title: Semiparametric Regression for Measurement Error Model with Heteroscedastic Error
Author(s): Mengyan Li* and Yanyuan Ma and Runze Li
Companies: and Penn State University and Penn State University
Keywords: Measurement error; Heteroscedasticity; B-splines; Semiparametrics; Efficient Score
Abstract:

Covariate measurement error is a common problem in many different studies. Improper treatment of measurement errors may affect the quality of estimation and the accuracy of inference. There has been extensive literature on the homoscedastic measurement error models. However, heteroscedastic measurement error issue is considered to be a difficult problem with less research available. In this paper, we consider a general parametric regression model allowing covariate measured with heteroscedastic error. We allow both the variance function of the measurement errors and the conditional density function of the error-prone covariate given the error-free covariates to be completely unspecified. We treat the variance function using B-splines approximation and propose a semiparametric estimator based on efficient score functions to deal with the heteroscedasticity of measurement error. The resulting estimator is consistent and enjoys good inference properties. Its finite sample performance is demonstrated through simulation studies and a real data example.


Authors who are presenting talks have a * after their name.

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