Iterative Distributed Multinomial Regression

[2412.01030] Iterative Distributed Multinomial Regression Skip to main content Search Submit Donate Log in Economics > Econometrics arXiv:2412.01030 (econ) [Submitted on 2 Dec 2024 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: Iterative Distributed Multinomial Regression Authors: Yanqin Fan , Yigit Okar , Xuetao Shi View PDF HTML (experimental) 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. Subjects: Econometrics (econ.EM) Cite as: arXiv:2412.01030 [econ.EM] (or arXiv:2412.01030v2 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2412.01030 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xuetao Shi [ view email ] [v1] Mon, 2 Dec 2024 01:25:39 UTC (38 KB) [v2] Fri, 28 Aug 2026 10:16:57 UTC (51 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2024-12 Change to browse by: econ References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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