Timothy Mutunga

Feature Selection in Over-dispersed Binary and Count Data Models Using Penalized Optimal Estimating Functions

Generalized linear models (GLMs) remain a core class of supervised machine learning models for binary and count responses, with feature selection commonly carried out through penalized likelihood or quasi-likelihood methods. This paper develops a feature-selection framework based on penalized Optimal Estimating Functio...