High-dimensional regression with a count response
Methodology
2024-09-16 v1 Statistics Theory
Statistics Theory
Abstract
We consider high-dimensional regression with a count response modeled by Poisson or negative binomial generalized linear model (GLM). We propose a penalized maximum likelihood estimator with a properly chosen complexity penalty and establish its adaptive minimaxity across models of various sparsity. To make the procedure computationally feasible for high-dimensional data we consider its LASSO and SLOPE convex surrogates. Their performance is illustrated through simulated and real-data examples.
Cite
@article{arxiv.2409.08821,
title = {High-dimensional regression with a count response},
author = {Or Zilberman and Felix Abramovich},
journal= {arXiv preprint arXiv:2409.08821},
year = {2024}
}