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Bayesian Inference for Spatial Beta Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of Iran, 2018 In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model
L. Kalhori Nadrabadi, M. Mohhamadzadeh
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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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Generalized fiducial inference for normal linear mixed models [PDF]
, 2012 While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more than a few variance components are lacking, especially in the unbalanced ...
Cisewski, Jessi, Hannig, Jan
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Generalized linear mixed models can detect unimodal species-environment relationships [PDF]
PeerJ, 2013 Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ...
Tahira Jamil, Cajo J.F. ter Braak
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Linear Mixed Models: Gum and Beyond
Measurement Science Review, 2014 In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models.
Arendacká Barbora +4 more
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The R Journal, 2017 Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error ...
Mollie E. Brooks +8 more
semanticscholar +1 more source
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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Robustness of linear mixed‐effects models to violations of distributional assumptions
Methods in Ecology and Evolution, 2020 Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make several assumptions,
H. Schielzeth +9 more
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Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]
, 2015 We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona +3 more
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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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