Modified BIC Criterion for Model Selection in Linear Mixed Models
Mathematics, 2023 Linear mixed-effects models are widely used in applications to analyze clustered, hierarchical, and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed model ...
Hang Lai, Xin Gao
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Fast and flexible linear mixed models for genome-wide genetics. [PDF]
PLoS Genetics, 2019 Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits.
Daniel E Runcie, Lorin Crawford
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Integrating advanced discrete choice models in mixed integer linear optimization
, 2021 The integration of customer behavioral models in operations research (OR) is appealing to operators and policy makers (the supply) because it provides a better understanding of the preferences of customers (the demand) while planning for their systems ...
M. Bierlaire, M. Pacheco
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Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
Behavior Research Methods, 2020 Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis.
Levi Kumle, M. Võ, Dejan Draschkow
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Accessible analysis of longitudinal data with linear mixed effects models
Disease Models & Mechanisms, 2022 Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design,
Jessica I. Murphy +2 more
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Federated generalized linear mixed models for collaborative genome-wide association studies
iScience, 2023 Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges.
Wentao Li +3 more
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A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]
, 2015 A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context.
Aas K +41 more
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Frontiers in Psychology, 2017 We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach.
Nicolas Haverkamp, André Beauducel
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MCMC methods for multi-response generalized linear mixed models
, 2010 Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
J. Hadfield
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Simple reparameterization to improve convergence in linear mixed models
Acta Agriculturae Slovenica, 2010 Slow convergence and mixing are one of the main problems of Markov chain Monte Carlo (McMC) algorithms applied to mixed models in animal breeding. Poor convergence is to a large extent caused by high posterior correlation between variance components and ...
Gregor GORJANC +3 more
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