生物识别与生物统计学杂志

Robust Logistic and Probit Methods for Binary and Multinomial Regression

Abstract

Tabatabai MA, Li H, Eby WM, Kengwoung-Keumo JJ, Manne U, Bae S, Fouad M and Karan P Singh

In this paper we introduce new robust estimators for the logistic and probit regressions for binary, multinomial, nominal and ordinal data and apply these models to estimate the parameters when outliers or influential observations are present. Maximum likelihood estimates don’t behave well when outliers or influential observations are present. One remedy is to remove influential observations from the data and then apply the maximum likelihood technique on the deleted data. Another approach is to employ a robust technique that can handle outliers and influential observations without removing any observations from the data sets. The robustness of the method is tested using real and simulated data sets.