Semiparametric analysis of linear transformation models with covariate measurement errors.

Web-based Supplementary materials for: “Semiparametric Analysis of Linear Transformation Models with Covariate Measurement Errors”

Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models

In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient condition...

Simultaneous Inference of Partially Linear Error-in-Covariate Models: an Application to the U.S. Gasoline Demand∗

In this paper, we conduct simultaneous inference of the non-parametric multivariate function of a partially linear model when the covariate terms in both parametric and non-parametric parts are subject to the Berkson measurement errors. Based on semiparametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The devel...

Wavelet Threshold Estimator of Semiparametric Regression Function with Correlated Errors

Wavelet analysis is one of the useful techniques in mathematics which is used much in statistics science recently. In this paper, in addition to introduce the wavelet transformation, the wavelet threshold estimation of semiparametric regression model with correlated errors with having Gaussian distribution is determined and the convergence ratio of estimator computed. To evaluate the wavelet th...

Non-parametric and semiparametric models for missing covariates in parametric regression Abstracts

s Robustness of covariate modeling for the missing covariate problem in parametric regression is studied under the MAR assumption. For a simple missing covariate pattern, non-parametric likelihood is proposed and is shown to yield a consistent and semiparametrically efficient estimator for the regression parameter. Total robustness is achieved in this situation. For more general missing covaria...