Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative...

Theory in time series analysis is often developed in the context of finite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is...

We consider theoretical bootstrap coupling techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of coupling bootstrap techniques are developed for additive models with both symmetric error...

AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.

This study considers the theoretical bootstrap “coupling” techniques for nonparametric robust smoothers and quantile regression, and we verify the bootstrap improvement. To handle the curse of dimensionality, a variant of “coupling” bootstrap techniques is developed for additive models...

Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative...

Theory in time series analysis is often developed in the context of finite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is of a more complex structure, it is...

Assume that we have two populations (X <Subscript>1</Subscript>,Y <Subscript>1</Subscript>) and (X <Subscript>2</Subscript>,Y <Subscript>2</Subscript>) satisfying two general nonparametric regression models Y <Subscript> j </Subscript>=m <Subscript> j </Subscript>(X <Subscript> j </Subscript>)+ε <Subscript> j </Subscript>, j=1,2, where m(⋅) is a smooth location function, ε <Subscript> j </Subscript> has zero location and the response Y <Subscript> j </Subscript> is possibly right-censored. In this paper, we propose...</subscript></subscript></subscript></subscript></subscript></subscript></subscript></subscript></subscript></subscript>