Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
Abstract
This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order. While classical large-sample theory provides asymptotic normality of the MLE under certain conditions, such classical results are expected to fail in high-dimensions as documented for the binary logistic case in the seminal work of Sur and Cand\`es [2019]. We address this issue in classification problems with 3 or more classes, by developing asymptotic normality and asymptotic chi-square results for the multinomial logistic MLE (also known as cross-entropy minimizer) on null covariates. Our theory leads to a new methodology to test the significance of a given feature. Extensive simulation studies on synthetic data corroborate these asymptotic results and confirm the validity of proposed p-values for testing the significance of a given feature.
Cite
@article{arxiv.2305.17825,
title = {Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions},
author = {Kai Tan and Pierre C. Bellec},
journal= {arXiv preprint arXiv:2305.17825},
year = {2023}
}