Iterative Distributed Multinomial Regression

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Bibliographic Details
Published in:	arXiv.org (Dec 2, 2024), p. n/a
Main Author:	Fan, Yanqin
Other Authors:	Okar, Yigit, Shi, Xuetao
Published:	Cornell University Library, arXiv.org
Subjects:	Asymptotic methods Iterative methods Maximum likelihood estimators Regression models Computing time Distributed processing
Online Access:	Citation/Abstract Full text outside of ProQuest
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Description
Abstract:	This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves significantly faster computation and, when initialized with a consistent estimator, attains asymptotic efficiency under a weak dominance condition. Additionally, we propose a parametric bootstrap inference procedure based on the iterative distributed estimator and establish its consistency. Extensive simulation studies validate the effectiveness of the proposed methods and highlight the computational efficiency of the iterative distributed estimator.
ISSN:	2331-8422
Source:	Engineering Database