Iterative Distributed Multinomial Regression

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書誌詳細
出版年:	arXiv.org (Dec 2, 2024), p. n/a
第一著者:	Fan, Yanqin
その他の著者:	Okar, Yigit, Shi, Xuetao
出版事項:	Cornell University Library, arXiv.org
主題:	Asymptotic methods Iterative methods Maximum likelihood estimators Regression models Computing time Distributed processing
オンライン･アクセス:	Citation/Abstract Full text outside of ProQuest
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その他の書誌記述
抄録:	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
ソース:	Engineering Database