Results 11 to 20 of about 604,873 (258)
Subset Selection for Linear Mixed Models [PDF]
Biometrics, 2022 AbstractLinear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covariates—while accounting for this structured dependence—remains a challenge. We introduce a Bayesian decision analysis for subset selection with LMMs. Using a Mahalanobis
Kowal DR.
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Federated generalized linear mixed models for collaborative genome-wide association studies [PDF]
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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Neighborhood-level heterogeneity in childhood morbidity through generalized linear mixed models [PDF]
Frontiers in Public HealthObjectiveChildhood morbidities are crucial for improving long-term public health outcomes. This study aimed to examine the existence of child-specific and regional variation in childhood morbidity based on the cross-cutting study of the Performance ...
Endeshaw A. Derso +8 more
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Network analysis of longitudinal electronic health records using linear mixed models [PDF]
BioData MiningBackground The accelerating development of healthcare data stored in electronic health records (EHRs) has created novel opportunities for biomedical research.
Marina Vargas-Fernández +3 more
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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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Bayesian Linear Mixed Models with Polygenic Effects
Journal of Statistical Software, 2018 We considered Bayesian estimation of polygenic effects, in particular heritability in relation to a class of linear mixed models implemented in R (R Core Team 2018).
Jing Hua Zhao +2 more
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Fitting Linear Mixed-Effects Models Using lme4
Journal of Statistical Software, 2015 Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in
Douglas Bates +3 more
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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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Model Selection in Linear Mixed Models
Statistical Science, 2013 Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate statistical models.
Müller, Samuel +2 more
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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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