Add to ORKGVarying-coefficient partially linear models are frequently used in statistical modeling. Yet, their estimation and inferences have not been systematically studied. This article proposes a profile least-squares technique for estimating parametric components. The asymptotic normality of the profile least-squares estimator is studied. The main focus is the examination of whether the generalized likelihood techniques that were developed by Fan, Zhang and Zhang (2001) are applicable to the testing problems in the parametric component of semiparametric models. Profile likelihoo
This paper is concerned with semiparametric efficient estimation of a generalized partially linear v...
In this paper we consider partially linear varying coefficient models. We provide semiparametric eff...
This paper studies the estimation of a varying-coefficient partially linear regression model which i...
This paper is concerned with the statistical inference on seemingly unrelated varying coefficient pa...
The generalized varying coefficient partially linear model with growing number of predictors arises ...
Empirical-likelihood-based inference for the nonparametric parts in semiparametric varying-coefficie...
We consider consistent estimation of partially linear panel data models with fixed effects. We propo...
AbstractThis paper studies the estimation of a varying-coefficient partially linear regression model...
AbstractThe purpose of this paper is two-fold. First, for the estimation or inference about the para...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
Profile likelihood is a popular method of estimation in the presence of a nuisance parameter. It is ...
Summary. Motivated by an analysis of a real data set in ecology, we consider a class of partially no...
In this paper, we consider the application of the empirical likelihood method to partially linear mo...
The complexity of semiparametric models poses new challenges to sta-tistical inference and model sel...
By constructing an adjusted auxiliary vector ingeniously, we propose an adjusted empirical likelihoo...
This paper is concerned with semiparametric efficient estimation of a generalized partially linear v...
In this paper we consider partially linear varying coefficient models. We provide semiparametric eff...
This paper studies the estimation of a varying-coefficient partially linear regression model which i...
This paper is concerned with the statistical inference on seemingly unrelated varying coefficient pa...
The generalized varying coefficient partially linear model with growing number of predictors arises ...
Empirical-likelihood-based inference for the nonparametric parts in semiparametric varying-coefficie...
We consider consistent estimation of partially linear panel data models with fixed effects. We propo...
AbstractThis paper studies the estimation of a varying-coefficient partially linear regression model...
AbstractThe purpose of this paper is two-fold. First, for the estimation or inference about the para...
Recently, Hjort and Claeskens (2003) developed an asymptotic theory for model selection, model avera...
Profile likelihood is a popular method of estimation in the presence of a nuisance parameter. It is ...
Summary. Motivated by an analysis of a real data set in ecology, we consider a class of partially no...
In this paper, we consider the application of the empirical likelihood method to partially linear mo...
The complexity of semiparametric models poses new challenges to sta-tistical inference and model sel...
By constructing an adjusted auxiliary vector ingeniously, we propose an adjusted empirical likelihoo...
This paper is concerned with semiparametric efficient estimation of a generalized partially linear v...
In this paper we consider partially linear varying coefficient models. We provide semiparametric eff...
This paper studies the estimation of a varying-coefficient partially linear regression model which i...