We study nonlinear regression models whose both response and predictors are measured with errors and distorted as single-index models of some observable confounding variables, and propose a multicovariate-adjusted procedure. We first examine the relationship between the observed primary...

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...

In this paper, we consider the estimation problem of a correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a multiplicative fashion by an observed confounding variable. Two estimators, the moment-based estimator and the direct plug-in...

This paper investigates identification and root-n-consistent estimation of a class of single-index panel data models in which the link function is unknown, the unobserved individual effects may be correlated with all the explanatory variables, and all the explanatory variables may be...

This paper proposes a single-index semiparametric model in which the unknown function has cross-sectional unit specific weights. The initial motivation comes from the search for a better measure of liquidity in stock trading which is captured by the unknown function here. The model is estimated...

Partial linear single-index model (PLSIM) is a flexible and applicable model when investigating the underlying relationship between the response and the multivariate covariates. Most previous studies on PLSIM concentrated on mean regression, based on least square or likelihood approach. In...