A new robust approach for the polytomous logistic regression model based on Rényi's pseudodistances

This paper presents a robust alternative to the maximum likelihood estimator (MLE) for the polytomous logistic regression model, known as the family of minimum R & egrave;nyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are parametrized by a tuning parameter alpha >= 0, and include the MLE as a special case when alpha = 0. These estimators, along with a family of RP-based Wald-type tests, are shown to exhibit superior performance in the presence of misclassification errors. The paper includes an extensive simulation study and a real data example to illustrate the robustness of these proposed statistics.