Robust Logistic and Probit Methods for Binary and Multinomial Regression

Journal of Biometrics & Biostatistics

article

Tabatabai MA¹  Li H²  Eby WM³  Kengwoung-Keumo JJ²  Manne U⁴  Bae S⁵  Fouad M⁵  Karan P Singh⁵ 

[1] School of Graduate Studies and Research, Meharry Medical College;Department of Mathematical Sciences, Cameron University;Department of Mathematics, New Jersey City University;Department of Pathology and Comprehensive Cancer Center, University of Alabama at Birmingham;Department of Medicine Division of Preventive Medicine and Comprehensive Cancer Center, University of Alabama at Birmingham

关键词: Robust Logistic;    Probit Methods;    Binary;    Multinomial Regression;   

DOI  :  10.4172/2155-6180.1000202

来源: Hilaris Publisher

【 摘 要 】

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.

【 授权许可】

Unknown