Infinitely imbalanced binomial regression and deformed exponential families
Statistics Theory
2013-04-23 v2 Machine Learning
Statistics Theory
Abstract
The logistic regression model is known to converge to a Poisson point process model if the binary response tends to infinitely imbalanced. In this paper, it is shown that this phenomenon is universal in a wide class of link functions on binomial regression. The proof relies on the extreme value theory. For the logit, probit and complementary log-log link functions, the intensity measure of the point process becomes an exponential family. For some other link functions, deformed exponential families appear. A penalized maximum likelihood estimator for the Poisson point process model is suggested.
Cite
@article{arxiv.1303.7297,
title = {Infinitely imbalanced binomial regression and deformed exponential families},
author = {Tomonari Sei},
journal= {arXiv preprint arXiv:1303.7297},
year = {2013}
}