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Iterative Distributed Multinomial Regression

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  • Yanqin Fan
  • Yigit Okar
  • Xuetao Shi

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.

Suggested Citation

  • Yanqin Fan & Yigit Okar & Xuetao Shi, 2024. "Iterative Distributed Multinomial Regression," Papers 2412.01030, arXiv.org.
    • Handle: RePEc:arx:papers:2412.01030
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    File URL: http://arxiv.org/pdf/2412.01030
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