Estimation in linear regression models with measurement errors subject to single-indexed distortion

11Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In this paper, we consider statistical inference for linear regression models when neither the response nor the predictors can be directly observed, but are measured with errors in a multiplicative fashion and distorted as single index models of observable confounding variables. We propose a semiparametric profile least squares estimation procedure to estimate the single index. Then we develop a global weighted least squares estimation procedure for parameters of linear regression models via the varying coefficient models. Asymptotic properties of the proposed estimators are established. The results combined with consistent estimators for the asymptotic variance can be employed to test whether the targeted parameters in the single index and linear regression models are significant. Finite-sample performance of the proposed estimators is assessed by simulation experiments. The proposed methods are also applied to a dataset from a Pima Indian diabetes data study. © 2012 Elsevier B.V. All rights reserved.

Cite

CITATION STYLE

APA

Zhang, J., Gai, Y., & Wu, P. (2013). Estimation in linear regression models with measurement errors subject to single-indexed distortion. Computational Statistics and Data Analysis, 59(1), 103–120. https://doi.org/10.1016/j.csda.2012.10.001

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free