Ridge estimation for multinomial logit models with symmetric side constraints

Faisal Zahid () and Gerhard Tutz

Computational Statistics, 2013, vol. 28, issue 3, 1017-1034

Abstract: In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category. When parameters are penalized, shrinkage of estimates should not depend on the reference category. In this paper we investigate ridge regression for the multinomial logit model with symmetric side constraints, which yields parameter estimates that are independent of the reference category. In simulation studies the results are compared with the usual maximum likelihood estimates and an application to real data is given. Copyright Springer-Verlag 2013

Keywords: Cross-validation; L2 penalty; Logistic regression; Multicategory response; Penalization; Reference category (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-012-0341-1

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