# Parsimonious Mixed Models

arXiv: https://arxiv.org/abs/1506.04967 · HTML: https://mlsanity.com/paper/1506.04967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2015.

## Built on by (most cited first, 3 of 3)

- [Prosodic Alignment for off-screen automatic dubbing](https://mlsanity.com/paper/2204.02530/) (2022)
- [When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma](https://mlsanity.com/paper/2503.07320/) (2025)
- [Sparse mixed linear modeling with anchor-based guidance for high-entropy alloy discovery](https://mlsanity.com/paper/2504.20354/) (2025)

## Abstract (arXiv)

The analysis of experimental data with mixed-effects models requires decisions about the specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers, and Tily, 2013 recommended fitting `maximal' models with all possible random effect components included. Estimation of maximal models, however, may not converge. We show that failure to converge typically is not due to a suboptimal estimation algorithm, but is a consequence of attempting to fit a model that is too complex to be properly supported by the data, irrespective of whether estimation is based on maximum likelihood or on Bayesian hierarchical modeling with uninformative or weakly informative priors. Importantly, even under convergence, overparameterization may lead to uninterpretable models. We provide diagnostic tools for detecting overparameterization and guiding model simplification.