Weak Convergence of Smoothed Empirical Processes

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On the optimal estimation of probability measures in weak and strong topologies (2013, background): “The question of whether (1) holds has been addressed for the kernel density estimator [41, 36, 16] and wavelet density estimator [18] where a uniform central limit theorem as stated above has been proved for various P -Donsker classes, F (and also for non-Donsker but pre-Gaussian classes in [27]…”
Improved kernel estimation of copulas: Weak convergence and goodness-of-fit testing (2009, unknown): “As our proof is a straightforward adaptation of the ideas used in van der Vaart and Wellner (2007), we would like to clarify one point.”
A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data (2012, methodology): “We may utilize some smoothing techniques for this case [28], but the choice of a proper smoothing parameter needs further research.”
Donsker-type theorems for nonparametric maximum likelihood estimators (2007, background): “See [28,37,42] as well as [26,27] for results of this kind.”