A General Framework for Random Effects Models for Binary, Ordinal, Count Type and Continuous Dependent Variables Including Variable Selection

TL;DR

This paper introduces a flexible random effects modeling framework that accommodates various response types, including continuous, discrete, and counts, without restrictive distributional assumptions, and addresses variable selection for high-dimensional effects.

Contribution

The paper proposes a unified mixed thresholds model for diverse response types and introduces methods for variable selection to achieve sparse representations in complex random effects models.

Findings

The model effectively handles mixed response types with better fit than traditional models.

Variable selection methods successfully identify sparse effect structures.

The framework is adaptable to various data types and modeling scenarios.

Abstract

A general random effects model is proposed that allows for continuous as well as discrete distributions of the responses. Responses can be unrestricted continuous, bounded continuous, binary, ordered categorical or given in the form of counts. The distribution of the responses is not restricted to exponential families, which is a severe restriction in generalized mixed models. Generalized mixed models use fixed distributions for responses, for example the Poisson distribution in count data, which has the disadvantage of not accounting for overdispersion. By using a response function and a thresholds function the proposed mixed thresholds model can account for a variety of alternative distributions that often show better fits than fixed distributions used within the generalized linear model framework. A particular strength of the model is that it provides a tool for joint modeling, responses may be of different types, some can be discrete, others continuous. In addition to introducing the mixed thresholds model parameter sparsity is addressed. Random effects models can contain a large number of parameters, in particular if effects have to be assumed as measurement-specific. Methods to obtain sparser representations are proposed and illustrated. The methods are shown to work in the thresholds model but could also be adapted to other modeling approaches.