Boosting in Univariate Nonparametric Maximum Likelihood Estimation
Machine Learning
2021-04-21 v1 Machine Learning
Signal Processing
Methodology
Abstract
Nonparametric maximum likelihood estimation is intended to infer the unknown density distribution while making as few assumptions as possible. To alleviate the over parameterization in nonparametric data fitting, smoothing assumptions are usually merged into the estimation. In this paper a novel boosting-based method is introduced to the nonparametric estimation in univariate cases. We deduce the boosting algorithm by the second-order approximation of nonparametric log-likelihood. Gaussian kernel and smooth spline are chosen as weak learners in boosting to satisfy the smoothing assumptions. Simulations and real data experiments demonstrate the efficacy of the proposed approach.
Cite
@article{arxiv.2101.08505,
title = {Boosting in Univariate Nonparametric Maximum Likelihood Estimation},
author = {YunPeng Li and ZhaoHui Ye},
journal= {arXiv preprint arXiv:2101.08505},
year = {2021}
}