Maximum softly-penalized likelihood for mixed effects logistic regression

TL;DR

This paper introduces a soft-penalization method for maximum likelihood estimation in mixed effects logistic regression, ensuring estimates stay within valid parameter space and retain desirable asymptotic properties.

Contribution

It proposes a novel scaled additive penalty approach that guarantees interior solutions and preserves asymptotic optimality in mixed effects logistic regression.

Findings

Penalized estimates avoid boundary issues.

Method maintains consistency and efficiency.

Superior finite sample performance demonstrated.

Abstract

Maximum likelihood estimation in logistic regression with mixed effects is known to often result in estimates on the boundary of the parameter space. Such estimates, which include infinite values for fixed effects and singular or infinite variance components, can cause havoc to numerical estimation procedures and inference. We introduce an appropriately scaled additive penalty to the log-likelihood function, or an approximation thereof, which penalizes the fixed effects by the Jeffreys' invariant prior for the model with no random effects and the variance components by a composition of negative Huber loss functions. The resulting maximum penalized likelihood estimates are shown to lie in the interior of the parameter space. Appropriate scaling of the penalty guarantees that the penalization is soft enough to preserve the optimal asymptotic properties expected by the maximum likelihood estimator, namely consistency, asymptotic normality, and Cram\'er-Rao efficiency. Our choice of penalties and scaling factor preserves equivariance of the fixed effects estimates under linear transformation of the model parameters, such as contrasts. Maximum softly-penalized likelihood is compared to competing approaches on two real-data examples, and through comprehensive simulation studies that illustrate its superior finite sample performance.