AbstractVarying coefficient models are useful extensions of the classical linear models. Under the condition that the coefficient functions possess about the same degrees of smoothness, the model can easily be estimated via simple local regression. This leads to the one-step estimation procedure. In this paper, we consider a semivarying coefficient model which is an extension of the varying coefficient model, which is called the semivarying-coefficient model. Procedures for estimation of the linear part and the nonparametric part are developed and their associated statistical properties are studied. The proposed methods are illustrated by some simulation studies and a real example
In this paper we propose a general series method to estimate a semiparametric partially linear varyi...
This paper studies a new class of semiparametric dynamic panel data models, in which some coefficien...
The complexity of semiparametric models poses new challenges to sta-tistical inference and model sel...
AbstractVarying coefficient models are useful extensions of the classical linear models. Under the c...
Varying coefficient models are useful extensions of the classical linear models. Under the condition...
We study a semi-varying coefficient model where the regressors are generated by the multivariate uni...
This paper studies the estimation of a varying-coefficient partially linear regression model which i...
This article deals with statistical inferences based on the varying-coefficient models proposed by H...
AbstractThis paper studies the estimation of a varying-coefficient partially linear regression model...
AbstractThe procedures for estimations of the parametric component and the nonparametric component o...
AbstractThis paper focuses on the variable selections for semiparametric varying coefficient partial...
In this paper we consider partially linear varying coefficient models. We provide semiparametric eff...
Varying-coefficient models are a useful extension of the classical linear models. The appeal of thes...
AbstractOne of the advantages for the varying-coefficient model is to allow the coefficients to vary...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
In this paper we propose a general series method to estimate a semiparametric partially linear varyi...
This paper studies a new class of semiparametric dynamic panel data models, in which some coefficien...
The complexity of semiparametric models poses new challenges to sta-tistical inference and model sel...
AbstractVarying coefficient models are useful extensions of the classical linear models. Under the c...
Varying coefficient models are useful extensions of the classical linear models. Under the condition...
We study a semi-varying coefficient model where the regressors are generated by the multivariate uni...
This paper studies the estimation of a varying-coefficient partially linear regression model which i...
This article deals with statistical inferences based on the varying-coefficient models proposed by H...
AbstractThis paper studies the estimation of a varying-coefficient partially linear regression model...
AbstractThe procedures for estimations of the parametric component and the nonparametric component o...
AbstractThis paper focuses on the variable selections for semiparametric varying coefficient partial...
In this paper we consider partially linear varying coefficient models. We provide semiparametric eff...
Varying-coefficient models are a useful extension of the classical linear models. The appeal of thes...
AbstractOne of the advantages for the varying-coefficient model is to allow the coefficients to vary...
Nonparametric and semiparametric regression models are useful statistical regression models to disco...
In this paper we propose a general series method to estimate a semiparametric partially linear varyi...
This paper studies a new class of semiparametric dynamic panel data models, in which some coefficien...
The complexity of semiparametric models poses new challenges to sta-tistical inference and model sel...