Results 11 to 20 of about 658,808 (305)
Prediction in Multivariate Mixed Linear Models [PDF]
JOURNAL OF THE JAPAN STATISTICAL SOCIETY, 2003 Summary: In the multivariate mixed linear model or multivariate components of variance model with equal replications, this paper addresses the problem of predicting the sum of the regression mean and the random effects. When the feasible best linear unbiased predictors or empirical Bayes predictors are used, this prediction problem reduces to the ...
Tatsuka Kubokawa, M. S. Srivastava
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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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BLMM: Parallelised computing for big linear mixed models [PDF]
NeuroImage, 2022 Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs.
Thomas Maullin-Sapey, Thomas E. Nichols
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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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POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models
Journal of Statistical Software, 2009 The POWERLIB SAS/IML software provides convenient power calculations for a widerange of multivariate linear models with Gaussian errors. The software includes the Box,Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the univariate" approach ...
Jacqueline L. Johnson +5 more
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partR2: partitioning R2 in generalized linear mixed models [PDF]
PeerJ, 2021 The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by ...
Martin A. Stoffel +2 more
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Gradient boosting for linear mixed models [PDF]
The International Journal of Biostatistics, 2021 Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory.
Griesbach, Colin +2 more
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, 2022 AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework.
Osval Antonio Montesinos López +2 more
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