Semiparametric regression for measurement error model with heteroscedastic error

JOURNAL OF MULTIVARIATE ANALYSIS

卷:171

Article

Li, Mengyan¹  Ma, Yanyuan¹  Li, Runze¹ 

[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA

关键词: B-splines;    Efficient score;    Heteroscedasticity;    Measurement error;    Semiparametrics;   

DOI  :  10.1016/j.jmva.2018.12.012

来源: Elsevier

【 摘 要 】

Covariate measurement error is a common problem. Improper treatment of measurement errors may affect the quality of estimation and the accuracy of inference. Extensive literature exists on homoscedastic measurement error models, but little research exists on heteroscedastic measurement. In this paper, we consider a general parametric regression model allowing for a 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-spline approximation and propose a semiparametric estimator based on efficient score functions to deal with the heteroscedasticity of the 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. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free