Empirical likelihood inference in partially linear single-index models for longitudinal data
Journal of Multivariate Analysis
Bias-corrected empirical likelihood in a multi-link semiparametric model
Journal of Multivariate Analysis
On a dimension reduction regression with covariate adjustment
Journal of Multivariate Analysis
Non-convex penalized estimation in high-dimensional models with single-index structure
Journal of Multivariate Analysis
A revisit to correlation analysis for distortion measurement error data
Journal of Multivariate Analysis
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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.