This paper presents a robust alternative to the Maximum Likelihood Estimator (MLE) for the Polytomous Logistic Regression Model (PLRM), known as the family of minimum R\`enyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are parametrized by a tuning parameter $\alpha\geq0$, 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.

翻译：本文提出了一种多项式逻辑回归模型（PLRM）中最大似然估计（MLE）的稳健替代方法，即最小Rényi伪距离（RP）估计量族。所提出的最小RP估计量由调节参数$\alpha\geq0$参数化，并在$\alpha=0$时包含MLE作为特例。研究表明，这些估计量及其基于RP的Wald型检验族在存在误分类误差时表现出优越的性能。本文通过大量仿真研究和实际数据案例，展示了所提统计量的稳健性。