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September 2015 Distributed multinomial regression
Matt Taddy
Ann. Appl. Stat. 9(3): 1394-1414 (September 2015). DOI: 10.1214/15-AOAS831


This article introduces a model-based approach to distributed computing for multinomial logistic (softmax) regression. We treat counts for each response category as independent Poisson regressions via plug-in estimates for fixed effects shared across categories. The work is driven by the high-dimensional-response multinomial models that are used in analysis of a large number of random counts. Our motivating applications are in text analysis, where documents are tokenized and the token counts are modeled as arising from a multinomial dependent upon document attributes. We estimate such models for a publicly available data set of reviews from Yelp, with text regressed onto a large set of explanatory variables (user, business, and rating information). The fitted models serve as a basis for exploring the connection between words and variables of interest, for reducing dimension into supervised factor scores, and for prediction. We argue that the approach herein provides an attractive option for social scientists and other text analysts who wish to bring familiar regression tools to bear on text data.


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Matt Taddy. "Distributed multinomial regression." Ann. Appl. Stat. 9 (3) 1394 - 1414, September 2015.


Received: 1 December 2013; Revised: 1 April 2015; Published: September 2015
First available in Project Euclid: 2 November 2015

zbMATH: 06525991
MathSciNet: MR3418728
Digital Object Identifier: 10.1214/15-AOAS831

Keywords: computational social science , distributed computing , Lasso , logistic regression , MapReduce , multinomial inverse regression , text analysis

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.9 • No. 3 • September 2015
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