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Iterative Distributed Multinomial Regression

Sparad:
I publikationen:arXiv.org (Dec 2, 2024), p. n/a
Huvudupphov: Fan, Yanqin
Övriga upphov: Okar, Yigit, Shi, Xuetao
Utgiven:
Cornell University Library, arXiv.org
Ämnen:
Asymptotic methods
Iterative methods
Maximum likelihood estimators
Regression models
Computing time
Distributed processing
Länkar:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3138995432 
045 0 |b d20241202 
100 1 |a Fan, Yanqin 
245 1 |a Iterative Distributed Multinomial Regression 
260 |b Cornell University Library, arXiv.org  |c Dec 2, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Asymptotic methods 
653 |a Iterative methods 
653 |a Maximum likelihood estimators 
653 |a Regression models 
653 |a Computing time 
653 |a Distributed processing 
700 1 |a Okar, Yigit 
700 1 |a Shi, Xuetao 
773 0 |t arXiv.org  |g (Dec 2, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3138995432/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.01030