A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression

A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression

When developing prediction models for small or sparse binary data with many highly correlated covariates, logistic regression often encounters separation or multicollinearity problems, resulting serious bias and even the nonexistence of standard maximum likelihood estimates. The combination of separ...

Celý popis

Podrobná bibliografie
Hlavní autoři: Ying Guan, Guang-Hui Fu
Médium: Článek
Jazyk:English
Vydáno: MDPI AG 2022-10-01
Edice:Mathematics
Témata:
double-penalized likelihood estimator
log<i>F</i>-type penalty
logistic regression
multicollinearity
separation
On-line přístup:https://www.mdpi.com/2227-7390/10/20/3824

Internet

https://www.mdpi.com/2227-7390/10/20/3824

Podobné jednotky