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Iterative Distributed Multinomial Regression

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Publié dans:arXiv.org (Dec 2, 2024), p. n/a
Auteur principal: Fan, Yanqin
Autres auteurs: Okar, Yigit, Shi, Xuetao
Publié:
Cornell University Library, arXiv.org
Sujets:
Asymptotic methods
Iterative methods
Maximum likelihood estimators
Regression models
Computing time
Distributed processing
Accès en ligne:Citation/Abstract
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Résumé: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