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Iterative Distributed Multinomial Regression

Yanqin Fan, Yigit Okar, Xuetao Shi

arXiv 2 Dec 2024 · Econometrics

arXiv:2412.01030 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

32
references
58
in-text mentions
32
distinct cited
1
self-citations
11,719
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Taddy, M (2015) Distributed multinomial regression0.874132100%
2Pastorello, S., V. Patilea, and E. Renault (2003) Iterative and recursive estimation in structural nonadaptive models0.8435360%
3Baker, S. G (1994) The multinomial-Poisson transformation0.73732100%
4Böhning, D. and B. G. Lindsay (1988) Monotonicity of quadratic-approximation algorithms0.73732100%
5Böhning, D (1992) Multinomial logistic regression algorithm0.73732100%
6Simon, N., J. Friedman, and T. Hastie (2013) A blockwise descent algorithm for group-penalized multiresponse and multinomial regression0.73732100%
7Gentzkow, M., J. M. Shapiro, and M. Taddy (2019) Measuring group differences in high-dimensional choices: method and application to congressional speech0.51121100%
8Taddy, M (2013) Multinomial inverse regression for text analysis0.51121100%
9Kelly, B. T., A. Manela, and A. Moreira (2019) Text selection, Working Paper 26517, National Bureau of Economic Research0.40511100%
10Aguirregabiria, V. and P. Mira (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete Markov decision models0.40511100%

Showing the top 10 of 32 scored citations.