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http://dx.doi.org/10.5351/KJAS.2005.18.3.701

Suppression for Logistic Regression Model  

Hong C. S. (Department of Statistics, Sungkyunkwan University)
Kim H. I. (CSC, KYOBO Auto Insurance Co.)
Ham J. H. (FUJI XEROX)
Publication Information
The Korean Journal of Applied Statistics / v.18, no.3, 2005 , pp. 701-712 More about this Journal
Abstract
The suppression for logistic regression models has been debated no longer than that for linear regression models since, among many other reasons, sum of squares for regression (SSR) or coefficient of determination ($R^2$) could be defined into various ways. Based on four kinds of $R^2$'s: two kinds are most preferred, and the other two are proposed by Liao & McGee (2003), four kinds of SSR's are derived so that the suppression for logistic models is explained. Many data fitted to logistic models are generated by Monte Carlo method. We explore when suppression happens, and compare with that for linear regression models.
Keywords
Bias; Coefficient of determination; Inherent Prediction error; Log-linear model; Logit model; SSR;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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