Improving the Effectiveness of Maximum Score Estimators for Binary Regression Models

Abstract

Maximum score estimation is a class of semiparametric methods for the
coefficients of regression models. Estimates are obtained by the maximization
of the special function, called the score. In case of binary regression models
it is the fraction of correctly classified observations. The aim of this article
is to propose a modification to the score function. The modification allows
to obtain smaller variances of estimators than the standard maximum score
method without impacting other properties like consistency. The study consists
of extensive Monte Carlo experiments.