The empirical likelihood method is especially useful for constructing confidence intervals or regions of the parameter of interest. This method has been extensively applied to linear regression and generalized linear regression models. In this paper, the empirical likelihood method for...

The problem of testing for the equality of k distribution functions under Case 2 interval censoring is studied and a supremum-type test statistic is proposed based on the differences between the nonparametric maximum likelihood estimator and the so-called leveraged bootstrap estimator of the k...

In this paper, from the estimating equation-based sufficient dimension reduction method in the literature, its robust version is proposed to alleviate the impact from outliers. To achieve this, a robust nonparametric regression estimator is suggested. The estimator is plugged in the estimating...

<Para ID="Par1">This paper is concerned about robust comparison of two regression curves. Most of the procedures in the literature are least-squares-based methods with local polynomial approximation to nonparametric regression. However, the efficiency of these methods is adversely affected by outlying...</para>

This paper aims at investigating model checking for parametric models with response missing at random which is a more general missing mechanism than missing completely at random. Different from existing approaches, two tests have normal distributions as the limiting null distributions no matter...

In this paper, we investigate checking the adequacy of varying coefficient models with response missing at random. In doing so, we first construct two completed data sets based on imputation and marginal inverse probability weighted methods, respectively. The empirical process-based tests by...

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...

To test heteroscedasticity in single index models, in this paper two test statistics are proposed via quadratic conditional moments. Without the use of dimension reduction structure, the first test has the usual convergence rate in nonparametric sense. Under the dimension reduction structure of...