Robust Model Selection for Finite Mixture of Regression Models Through Trimming
Methodology
2019-05-06 v1
Abstract
In this article, we introduce a new variable selection technique through trimming for finite mixture of regression models. Compared to the traditional variable selection techniques, the new method is robust and not sensitive to outliers. The estimation algorithm is introduced and numerical studies are conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods.
Cite
@article{arxiv.1905.01036,
title = {Robust Model Selection for Finite Mixture of Regression Models Through Trimming},
author = {Sijia Xiang and Weixin Yao},
journal= {arXiv preprint arXiv:1905.01036},
year = {2019}
}