Articolo in rivista, 2020, ENG, 10.1214/20-SS128

Flexible, boundary adapted, nonparametric methods for the estimation of univariate piecewise-smooth functions

Amato, Umberto; Antoniadis, Anestis; De Feis, Italia

CNR; Univ Grenoble Alpes; Univ Cape Town; CNR

We present and compare some nonparametric estimation methods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a fixed equidistant or not equidistant design regression model and a random design model.

Statistics surveys 14 , pp. 32–70

Keywords

Wavelets, boundary corrections, nonparametric regression, smoothing splines, thresholding, model selection, backfitting

CNR authors

Amato Umberto, De Feis Italia

CNR institutes

IAC – Istituto per le applicazioni del calcolo "Mauro Picone"