With many real-world examples, this book shows how to apply the powerful methods of
smoothing splines in practice. It covers basic smoothing spline models as well as more …

Standard approaches to constructing nonparametric confidence bands for functions are
frustrated by the impact of bias, which generally is not estimated consistently when using the …

We propose a simple framework for estimating derivatives without _tting the regression
function in nonparametric regression. Unlike most existing methods that use the symmetric …

A new method is proposed for estimating derivatives of a nonparametric regression function.
By applying Taylor expansion technique to a derived symmetric difference sequence, we …

In this paper we consider the problem of estimating E [(Y− E [Y∣ X]) 2] based on a finite
sample of independent, but not necessarily identically distributed, random variables …

It is known that for nonparametric regression, local linear composite quantile regression
(local linear CQR) is a more competitive technique than classical local linear regression …

Random forest, a data-mining technique which uses multiple classification or regression
trees, is a popular algorithm used for prediction. Inference and goodness-of-fit assessment …

The problem of residual variance estimation consists of estimating the best possible
generalization error obtainable by any model based on a finite sample of data. Even though …

In this paper, we propose simultaneous confidence bands for the nonparametric link function
in single-index models in the presence of a nuisance index parameter. We establish the …

We study the least squares estimator in the residual variance estimation context. We show
that the mean squared differences of paired observations are asymptotically normally …