Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints

Zahid, Faisal Maqbool and Tutz, Gerhard (September 2009): Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints. Department of Statistics: Technical Reports, No.67

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.

• Item Type: Paper (Technical Report)
• Keywords: logistic regression, penalization, side constraints, ridge regression, cross-validation, multinomial logit
• Subjects: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Dewey Classification: 600 Natural sciences and mathematics > 510 Mathematics
• Language: English