In this paper we consider partially linear varying coefficient models. We provide semiparametric efficient estimators of the parametric part as well as rate-optimal estimators of the nonparametric part. In our model, different nonparametric coefficients have different smoothing variables. This...

In the context of the Fay–Herriot model, a mixed regression model routinely used to combine information from various sources in small area estimation, certain adjustments to a standard likelihood (e.g., profile, residual, etc.) have been recently proposed in order to produce strictly positive...

We introduce the concept of local moments for a distribution in p, p[greater-or-equal, slanted]1, at a point z[set membership, variant]p. Local moments are defined as normalized limits of the ordinary moments of a truncated version of the distribution, ignoring the probability mass falling...

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...

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...

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...

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...

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...

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...