Although generalized cross-validation (GCV) has been frequently applied to select bandwidth when kernel methods are used to estimate non-parametric mixed-effect models in which non-parametric mean functions are used to model covariate effects, and additive random effects are applied to account...

In this paper, a two-stage estimation method for non-parametric additive models is investigated. Differing from Horowitz and Mammen's two-stage estimation, our first-stage estimators are designed not only for dimension reduction but also as initial approximations to all of the additive...

In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of...

First, to test the existence of random effects in semiparametric mixed models (SMMs) under only moment conditions on random effects and errors, we propose a very simple and easily implemented non-parametric test based on a difference between two estimators of the error variance. One test is...

type="main" xml:id="sjos12044-abs-0001" <title type="main">ABSTRACT</title>The purpose of this article is threefold. First, variance components testing for ANOVA-type mixed models is considered, in which response may not be divided into independent sub-vectors, whereas most of existing methods are for models where...