Browsing by Author "Erees, Suay"

Now showing 1 - 2 of 2

Citation - Scopus: 2
Effects of dichotomizing continuous outcome on efficiencies of measures of explained variation in logistic regression: Simulation study and application
(Taylor and Francis Ltd., 2022) Suay Erees; Erees, Suay
Dichotomizing continuous outcome variables is a common procedure in medical sciences. When analyzing these variables using binary logistic regression great attention should be paid to the choice of the measure of explained variation ((Formula presented.). Since there are many different R 2 in logistic regression in order to make correct inferences about models evaluating their performances has become more important. The purpose of this paper is to reveal asymptotically more efficient and reliable R 2 measure when analyzing the models with dichotomized outcome. The eight most recommended R 2 statistics and ordinary least squares R 2 associated with the underlying continuous outcome have been included. Their asymptotic distributions have been studied. They have also been compared under varying correlational conditions between outcome and covariate. Extensive simulations using the bootstrap method have been conducted under two modeling scenarios. A real data example is also presented. The findings provide support and important basis for making efficient decisions. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Influences of misspecification on asymptotic relative efficiency of coefficients of determination: Application to agriculture
(TAYLOR & FRANCIS INC, 2017) Suay Erees; Aylin Alin; Alin, Aylin; Erees, Suay
Violation of correct specification may cause some undesirable results such as biased logistic regression coefficients and less efficient test statistics. In this paper asymptotic relative efficiency (ARE) of various coefficients of determination in misspecified binary logistic regression models is investigated. Seven types of misspecification have been included. ARE of test statistics for exponential and Weibull distributions as a method of calculating optimal cutpoints is derived to demonstrate misspecification. Theoretical relationships between coefficients of determination have also been analyzed. Extensive simulations using bootstrap method and a real data application reveal more efficient one under various modeling scenarios.