Quantile regression is gradually emerging as a unified statistical methodology for estimating
models of conditional quantile functions. By complementing the exclusive focus of classical …

The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a
procedure which can be interpreted as? tting a linear relationship to contaminated observed …

There are many books on various aspects of nonparametric inference such as density
estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard …

The theory of empirical processes provides valuable tools for the development of asymptotic
theory in (nonparametric) statistical models, and makes it possible to give a unified treatment …

Supplement to “Adaptive piecewise polynomial estimation via trend filtering”. We provide
proofs for the results in Sections 3 and 4. We also present the underlying theoretical …

This paper presents an account of the current state of sampling, 50 years after Shannon's
formulation of the sampling theorem. The emphasis is on regular sampling, where the grid is …

We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE,
for the change point problem in exponential family regression. An unknown step function is …

We introduce a family of adaptive estimators on graphs, based on penalizing the l 1 norm of
discrete graph differences. This generalizes the idea of trend filtering (Kim et al., 2009; …

Imaging is an interdisciplinary research area with profound applications in many areas of
science, engineering, technology, and medicine. The most primitive form of imaging is visual …

A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional
discrete signals, by solving the total variation regularized least-squares problem or the …