A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion
Computation
2021-09-15 v2
Abstract
We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L criterion. In addition to introducing an algorithm for performing LE regression, our framework enables robust regression with the L criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available non-robust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples. Supplementary materials for this article are available online.
Cite
@article{arxiv.2010.04133,
title = {A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion},
author = {Jocelyn T. Chi and Eric C. Chi},
journal= {arXiv preprint arXiv:2010.04133},
year = {2021}
}