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...

In a partial linear model, some non-stochastic linear restrictions are imposed under a multicollinearity setting. Semiparametric ridge and non-ridge type estimators, in a restricted manifold are defined. For practical use, it is assumed that the covariance matrix of the error term is unknown and...

In this paper bootstrap confidence bands are constructed for nonparametric quantile estimates of regression functions, where resampling is done from a suitably estimated empirical distribution function (edf) for residuals. It is known that the approximation error for the confidence band by the...

Semiparametric proportional hazard regression models are the cornerstone in modern survival analysis. Most estimation methodologies developed in the literature, such as the famous partial likelihood based estimation, are built on the ground that the censoring is noninformative. However, in many...

We propose jackknife empirical likelihood (EL) methods for constructing confidence intervals of mean with regression imputation that allows ignorable or nonignorable missingness. The confidence interval is constructed based on the adjusted jackknife pseudo-values (Rao and Shao, 1992). The...

In this article, a novel adaptive estimation is proposed for varying coefficient models. Unlike the traditional least squares based methods, the proposed approach can adapt to different error distributions. An efficient EM algorithm is provided to implement the proposed estimation. The...

This paper is concerned with the ridge estimation of the parameter vector β in partial linear regression model yi=xiβ+f(ti)+ϵi,1≤i≤n, with correlated errors, that is, when Cov(ϵ)=σ2V, with a positive definite matrix V and ϵ=(ϵ1,…,ϵn), under the linear constraint Rβ=r, for a given...

In this paper, ridge and non-ridge type shrinkage estimators and their positive parts are defined in the semiparametric regression model when the errors are dependent and some non-stochastic linear restrictions are imposed under a multicollinearity setting. The exact risk expressions in addition...