This paper considers local median estimation in fixed design regression problems. The proposed method is employed to estimate the median function and the variance function of a heteroscedastic regression model. Strong convergence rates of the proposed estimators are obtained. Simulation results axe given to show the performance of the proposed methods.MathematicsSCI(E)中国科学引文数据库(CSCD)1ARTICLE128-382
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametr...
This paper considers statistical inference for the heteroscedastic varying coefficient model. We pro...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...
When the data used to fit an heteroscedastic nonparametric regression model are contaminated with ou...
Variance function estimation in multivariate nonparametric regression is considered and the minimax ...
We consider estimation of mean and variance functions with kernel-weighted local polynomial fitting ...
This thesis is focused on local polynomial smoothers of the conditional vari- ance function in a het...
The regression model has been given a considerable amount of attention and played a significant role...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
The conditional variance function in a heteroscedastic, nonparametric regression model is estimated ...
Variance function estimation in nonparametric regression is considered. We derived the minimax rate ...
We introduce the extension of local polynomial fitting to the linear heteroscedastic regression mode...
In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametri...
Variance function estimation in multivariate nonparametric regression is considered and the minimax ...
Traditionally, non-parametric regression research has been centered on the mean estimation problem. ...
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametr...
This paper considers statistical inference for the heteroscedastic varying coefficient model. We pro...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...
When the data used to fit an heteroscedastic nonparametric regression model are contaminated with ou...
Variance function estimation in multivariate nonparametric regression is considered and the minimax ...
We consider estimation of mean and variance functions with kernel-weighted local polynomial fitting ...
This thesis is focused on local polynomial smoothers of the conditional vari- ance function in a het...
The regression model has been given a considerable amount of attention and played a significant role...
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regressi...
The conditional variance function in a heteroscedastic, nonparametric regression model is estimated ...
Variance function estimation in nonparametric regression is considered. We derived the minimax rate ...
We introduce the extension of local polynomial fitting to the linear heteroscedastic regression mode...
In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametri...
Variance function estimation in multivariate nonparametric regression is considered and the minimax ...
Traditionally, non-parametric regression research has been centered on the mean estimation problem. ...
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametr...
This paper considers statistical inference for the heteroscedastic varying coefficient model. We pro...
This paper studies robust estimation of multivariate regression model using kernel weighted local li...