Mixed-Effects Models

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Mixed-effects models study data grouped by people, sites, tasks, or other units, combining shared effects with group-specific deviations to account for dependence and heterogeneity. Common approaches include linear and generalized linear mixed models, Bayesian and likelihood-based estimation, and extensions using Gaussian processes, boosted trees, or neural networks for nonlinear relationships. They are used in settings such as longitudinal health studies, multimodal data, crop trials, and repeated measurements, with methods ranging from variational inference and Laplace approximation to scalable Krylov algorithms. A central challenge is balancing flexible predictions and computational scalability with interpretable effects, reliable uncertainty estimates, and stable inference when there are many random effects.

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20 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity
Oct 11, 2026Mahmud Hasan, Niloofar Ramezani, Hrishikesh ChakrabortyMixed-Effects Models Clinical Outcome Prediction
Department of Mathematics and Statistics, Penn State Altoona, PA, USA · Department of Biostatistics, Virginia Commonwealth University, VA, USA · Department of Biostatistics & Bioinformatics, Duke University, NC, USA
Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects
Oct 6, 2026Tianxi Li, Jie DingLLM Evaluation LLM-as-a-Judge
School of Statistics, University of Minnesota
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Sep 23, 2026Andrea Nava, Ken Rölli, Armin Begic +1Meta-Learning Neural Processes
Seminar for Statistics, ETH Zurich, Switzerland · Lucerne University of Applied Sciences and Arts, Switzerland
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Sep 21, 2026Alex Kipnis, Marcel Binz, Eric SchulzNeural Posterior Estimation Amortized Inference
Institute for Human-Centered AI, Helmholtz Munich, Germany
Connecting Score Matching, Maximum Likelihood, and Expectation-Maximization in Mixed Linear Regression
Sep 4, 2026Zhankun Luo, Abolfazl HashemiScore Matching Mixed-Effects Models
School of Electrical and Computer Engineering Purdue University, West Lafayette, IN, USA
Implementing neural network mixed-effects models in Template Model Builder (TMB)
Aug 31, 2026Nan Zheng, Hoi Yiu Cheung, Vibhu Sharma +2Mixed-Effects Models
Department of Mathematics and Statistics, Memorial University of Newfoundland, Canada · Resource Ecology and Fisheries Management, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, USA · Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute of Memorial University of Newfoundland, Canada
Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory
Aug 30, 2026Zhe Aurore Li, Quentin Clairon, Cécilia Samieri +3Mixed-Effects Models
Univ. Bordeaux, Inserm, BPH, UMR 1219, 33000 Bordeaux, France · INRIA SISTM, 33000 Bordeaux, France · Vaccine Research Institute, 94010 Creteil, France
A Repeated Measurements Approach to $SoH$ Battery Modelling of Cyclic Aged Data in a Laboratory Environment
Aug 20, 2026Mark Cary, Charles BokorLithium-Ion Batteries Mixed-Effects Models
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK · School of Engineering, Computing and Mathematics, Oxford Brookes University Oxford OX33 1HX, UK
Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data
Aug 6, 2026Nina van Gerwen, Dimitris Rizopoulos, Manon Hillegers +2Learning with Missing Data Mixed-Effects Models
Department of Biostatistics, Erasmus University Medical Center, the Netherlands · Department of Epidemiology, Erasmus University Medical Center, the Netherlands · Department of Child Psychiatry, Erasmus University Medical Center, the Netherlands +1
Structure Learning on Clustered Data
Jul 9, 2026Ryan Thompson, Matt P. Wand, Veerabhadran BaladandayuthapaniCausal Structure Learning Causal Identifiability
School of Mathematical and Physical Sciences University of Technology Sydney Ultimo, NSW 2007, Australia · Department of Biostatistics University of Michigan Ann Arbor, MI 48105, USA
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Jul 6, 2026Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2Bayesian Inference Hierarchical Bayesian Modeling
Department of Biostatistics & Bioinformatics, Duke University · Department of Ophthalmology, University of Miami
DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data
Jun 12, 2026Amartya BhattacharyaIrregular Time-Series Modeling Hierarchical Bayesian Modeling
Geisel School of Medicine, Dartmouth College
How Sensitive Are Radiomic AI Models to Acquisition Parameters?
May 14, 2026D. Gil, I. Sanchez, C. SanchezMedical Imaging Medical Diagnosis
Universitat Autònoma de Barcelona and Computer Vision Center, Edifici O, Campus UAB, Bellaterra (Cerdanyola), 08193, Barcleona, Spain
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Dynamical Systems Bayesian Inference
Inria SISTM team · Univ. Bordeaux, INSERM BPH, U1219, Inria SISTM team, VRI, France · Aalto University
MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction
May 6, 2026Aike Potze, Fred van Eeuwijk, Ioannis N. AthanasiadisMixed-Effects Models Crop Yield Prediction
Artificial Intelligence Group, Wageningen University & Research · Mathematical & Statistical Methods Group (Biometris), Wageningen University & Research
Random-Effects Algorithm for Random Objects in Metric Spaces
May 4, 2026Marcos Matabuena, Mateo CámaraMixed-Effects Models
Mohamed bin Zayed University of Artificial Intelligence · Signal Processing Applications Group, Universidad Politécnica de Madrid
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
Apr 28, 2026Mohamed Tarek, Pedro AfonsoExpectation-Maximization Variational Inference
PumasAI, USA
Neural Generalized Mixed-Effects Models
Apr 13, 2026Yuli Slavutsky, Sebastian Salazar, David M. BleiMixed-Effects Models Generalized Linear Models
Department of Statistics, Columbia University, New York, USA · Department of Computer Science, Columbia University, New York, USA
Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data
Oct 31, 2025Mitchell L. Prevett, Francis K. C. Hui, Zhi Yang Tho +2Gradient-Boosted Decision Trees Heteroscedastic Regression
Research School of Finance, Actuarial Studies and Statistics The Australian National University Canberra, ACT 2601, Australia
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
May 14, 2025Pascal Kündig, Fabio SigristLatent Variable Models Krylov Subspace Methods
Lucerne University of Applied Sciences and Arts · University of Basel · Seminar for Statistics, ETH Zurich

Oct 11, 2026J/K move · Enter open · S save

Deep Neural Mixed Models for Nonlinear Longitudinal Prediction with Subject-Specific Heterogeneity

Mahmud Hasan, Niloofar Ramezani, Hrishikesh Chakraborty

Department of Mathematics and Statistics, Penn State Altoona, PA, USA · Department of Biostatistics, Virginia Commonwealth University, VA, USA · Department of Biostatistics & Bioinformatics, Duke University, NC, USA

Mixed-Effects Models Clinical Outcome Prediction

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Longitudinal health data often exhibit within-subject correlation, subject-specific heterogeneity, and complex nonlinear predictor--response relationships that may not be adequately captured by conventional mixed models. We propose a Deep Generalized Neural Network Mixed Model (DGNMM) that extends the GLMM framework by using a deep neural network to learn nonlinear population-level relationships while incorporating directly trainable, regularized subject-specific effects to capture individual heterogeneity. The model is estimated using prediction-oriented stochastic optimization with separate regularization of the neural-network parameters and subject-specific effects. This formulation allows us to investigate when increased nonlinear representation capacity provides additional predictive benefit in the presence of subject-specific heterogeneity. A simulation study varies population-level nonlinearity and subject-specific heterogeneity and compares DGNMM with GLMM, artificial neural network (ANN), Maity--Pal (MP), and GNMM alternatives for continuous and binary longitudinal outcomes. In the real-data application, DGNMM achieved the lowest MSPE for continuous depression and anxiety prediction, while alternative models remained competitive for binary outcomes. Exploratory subject-level analyses showed heterogeneous predictive gains across individuals, but the magnitude of the estimated subject-specific effects was not significantly associated with predictive gain. Overall, increased nonlinear representation capacity was most beneficial for continuous prediction under nonlinear response structures, while greater subject-specific heterogeneity did not systematically yield larger predictive gains.