Multiclass-penalized logistic regression

Didier Nibbering and Trevor J. Hastie

Computational Statistics & Data Analysis, 2022, vol. 169, issue C

Abstract: A multinomial logistic regression model that penalizes the number of class-specific parameters is proposed. The number of parameters in a standard multinomial regression model increases linearly with the number of classes and number of explanatory variables. The multiclass-penalized regression model clusters parameters together by penalizing the differences between class-specific parameter vectors, instead of penalizing the number of explanatory variables. The model provides interpretable parameter estimates, even in settings with many classes. An algorithm for maximum likelihood estimation in the multiclass-penalized regression model is discussed. Applications to simulated and real data show in- and out-of-sample improvements in performance relative to a standard multinomial regression model.

Keywords: Multinomial logistic regression; Lasso; Parameter clustering (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:169:y:2022:i:c:s0167947321002486

DOI: 10.1016/j.csda.2021.107414

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