Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

Robust statistics is an extension of classical statistics that specifically takes into account the
concept that the underlying models used to describe data are only approximate. Its basic …

This review is a primer for those who wish to familiarize themselves with nonparametric
econometrics. Though the underlying theory for many of these methods can be daunting for …

Nonparametric regression models offer a way to understand and quantify relationships
between variables without having to identify an appropriate family of possible regression …

This paper studies the nonparametric modal regression problem systematically from a
statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of …

Robust functional principal components: A projection-pursuit approach Page 1 The Annals of
Statistics 2011, Vol. 39, No. 6, 2852–2882 DOI: 10.1214/11-AOS923 © Institute of Mathematical …

Proposals given in the field of ROC curves focusing on their robust aspects and
contributions are considered. The motivation is the extended belief that ROC curves are …

In this paper, the smoothing parameter selection problem has been examined in respect to a
smoothing spline implementation in predicting nonparametric regression models. For this …

The penalized logistic regression (PLR) is a powerful statistical tool for classification. It has
been commonly used in many practical problems. Despite its success, since the loss …

We propose a robust curve and surface estimator based on M-type estimators and penalty-
based smoothing. This approach also includes an application to wavelet regression. The …