Abstract

The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. Here we consider an alternative model under which the observed response is strongly related but not equal to the unobservable true response. We call this the hidden logistic regression (HLR) model because the unobservable true responses are comparable to a hidden layer in a feedforward neural net. We propose the maximum estimated likelihood method in this model, which is robust against separation unlike existing methods for logistic regression. We also consider outlier-robust estimation in this setting.

Keywords

Logistic regression, Hidden layer, Overlap, Robustness.