LOJİSTİK REGRESYONDA BİR AÇIKLAYICI DEĞİŞKENİN YANLIŞ TANUMLANMASININ BELİRTME KATSAYILARI ÜZERİNDEKİ ETKİLERİ

Abstract

Misspecifying an explanatory variable is a common problem in logistic regression as it is for all members of generalized linear models. Categorizing a continuous explanatory variable, using wrong functional form of an explanatory variable and omitting an explanatory variable from the model are commonly made misspecifications in logistic regression analysis. Studies show that all of these cases cause a loss in efficiency for test statistics. In this paper, the effects of these types of misspecification on asymptotic relative efficiency of different coefficients of determination are investigated. All calculations and comparisons are based on extensive simulation study using bootstrap methods