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...
| Main Authors: | , |
|---|---|
| 格式: | 文件 |
| 语言: | English |
| 出版: |
MDPI AG
2022-10-01
|
| 丛编: | Mathematics |
| 主题: | |
| 在线阅读: | https://www.mdpi.com/2227-7390/10/20/3824 |