Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression

Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression

High-dimensional classification studies have become widespread across various domains. The large dimensionality, coupled with the possible presence of data contamination, motivates the use of robust, sparse estimation methods to improve model interpretability and ensure the majority of observations...

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Bibliographic Details
Main Authors: Luca Insolia, Ana Kenney, Martina Calovi, Francesca Chiaromonte
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Stats
Subjects:
classification
logistic slippage model
mixed-integer conic programming
model selection
honey bee loss
outlier detection
Online Access:https://www.mdpi.com/2571-905X/4/3/40

Internet

https://www.mdpi.com/2571-905X/4/3/40

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