Multivariate Intensity Estimation via Hyperbolic Wavelet Selection
Statistics Theory
2016-11-28 v2 Applications
Statistics Theory
Abstract
We propose a new statistical procedure able in some way to overcome the curse of dimensionality without structural assumptions on the function to estimate. It relies on a least-squares type penalized criterion and a new collection of models built from hyperbolic biorthogonal wavelet bases. We study its properties in a unifying intensity estimation framework, where an oracle-type inequality and adaptation to mixed smoothness are shown to hold. Besides, we describe an algorithm for implementing the estimator with a quite reasonable complexity.
Cite
@article{arxiv.1611.07237,
title = {Multivariate Intensity Estimation via Hyperbolic Wavelet Selection},
author = {Nathalie Akakpo},
journal= {arXiv preprint arXiv:1611.07237},
year = {2016}
}