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...

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Detalhes bibliográficos
Principais autores: Ying Guan, Guang-Hui Fu
Formato: Artigo
Idioma:English
Publicado em: MDPI AG 2022-10-01
coleção:Mathematics
Assuntos:
double-penalized likelihood estimator
log<i>F</i>-type penalty
logistic regression
multicollinearity
separation
Acesso em linha:https://www.mdpi.com/2227-7390/10/20/3824

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