This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent
advances regarding confidence bands and geometric/topological features. We begin with a …

Provides a unified account of the most popular approaches to nonparametric regression
smoothing. This edition contains discussions of boundary corrections for trigonometric series …

This is the second volume of a text on the theory and practice of maximum penalized
likelihood estimation. It is intended for graduate students in statistics, operations research …

Anti-concentration and honest, adaptive confidence bands Page 1 The Annals of Statistics
2014, Vol. 42, No. 5, 1787–1818 DOI: 10.1214/14-AOS1235 © Institute of Mathematical …

We consider statistical inference of trends in mean non-stationary models. A test statistic is
proposed for the existence of structural breaks in trends. On the basis of a strong invariance …

In the North Atlantic Ocean, flow of North Atlantic Deep Water (NADW), and of its ancient
counterpart Northern Component Water (NCW), across the Greenland‐Scotland Ridge …

Confidence bands for regression curves and their first p derivatives are obtained via local
pth order polynomial estimation. The method allows for multiparameter local likelihood …

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 …

This paper studies higher-order inference properties of nonparametric local polynomial
regression methods under random sampling. We prove Edgeworth expansions for t statistics …

In this article we construct simultaneous confidence bands for a smooth curve using
penalized spline estimators. We consider three types of estimation methods:(a) as a …