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http://dx.doi.org/10.5351/KJAS.2007.20.3.551

Algorithm for the Robust Estimation in Logistic Regression  

Kim, Bu-Yong (Department of Statistics, Sookmyung Women's University)
Kahng, Myung-Wook (Department of Statistics, Sookmyung Women's University)
Choi, Mi-Ae (Computer Systems Division, Samsung Electronics Co.)
Publication Information
The Korean Journal of Applied Statistics / v.20, no.3, 2007 , pp. 551-559 More about this Journal
Abstract
The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.
Keywords
Logistic regression; outlier identification; V-mask criterion; robust estimation;
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Times Cited By KSCI : 1  (Citation Analysis)
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