alexa Robust Logistic and Probit Methods for Binary and Multinomial Regression
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Robust Logistic and Probit Methods for Binary and Multinomial Regression

Tabatabai MA1, Li H2, Eby WM3, Kengwoung-Keumo JJ2, Manne U4, Bae S5, Fouad M5 and Singh KP5*

1School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA

2Department of Mathematical Sciences, Cameron University, Lawton, OK 73505, USA

3Department of Mathematics, New Jersey City University, Jersey City, NJ 07305, USA

4Department of Pathology and Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA

5Department of Medicine Division of Preventive Medicine and Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA

*Corresponding Author:
Karan P Singh
Department of Medicine Division of Preventive Medicine
and Comprehensive Cancer Center
University of Alabama at Birmingham, Birmingham, AL 35294, USA
Tel: 205-934-6887
Fax: 205-934-4262
E-mail: [email protected]

Received date: July 01, 2014; Accepted date: July 27, 2014; Published date: July 30, 2014

Citation: Tabatabai MA, Li H, Eby WM, Kengwoung-Keumo JJ, Manne U, et al. (2014) Robust Logistic and Probit Methods for Binary and Multinomial Regression. J Biomet Biostat 5:202. doi:10.4172/2155-6180.1000202

Copyright: © 2014 Tabatabai MA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

Abstract

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.

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