Two-parameter ridge estimator in the binary logistic regression
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
TAYLOR & FRANCIS INC
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The binary logistic regression is a commonly used statistical method when the outcome variable is dichotomous or binary. The explanatory variables are correlated in some situations of the logit model. This problem is called multicollinearity. It is known that the variance of the maximum likelihood estimator (MLE) is inflated in the presence of multicollinearity. Therefore, in this study, we define a new two-parameter ridge estimator for the logistic regression model to decrease the variance and overcome multicollinearity problem. We compare the new estimator to the other well-known estimators by studying their mean squared error (MSE) properties. Moreover, a Monte Carlo simulation is designed to evaluate the performances of the estimators. Finally, a real data application is illustrated to show the applicability of the new method. According to the results of the simulation and real application, the new estimator outperforms the other estimators for all of the situations considered.
Açıklama
Anahtar Kelimeler
Logistic regression, MLE, Monte Carlo simulation, MSE, Multicollinearity, Ridge estimator
Kaynak
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
46
Sayı
9
