Results 11 to 20 of about 3,822,020 (321)
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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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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"Prediction in Multivariate Mixed Linear Models" [PDF]
JOURNAL OF THE JAPAN STATISTICAL SOCIETY, 2003 The multivariate mixed linear model or multivariate components of variance model with equal replications is considered.The paper addresses the problem of predicting the sum of the regression mean and the random e ects.When the feasible best linear ...
M. S. Srivastava, Tatsuka Kubokawa
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Variational Bayesian Inference in High-Dimensional Linear Mixed Models
Mathematics, 2022 In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler.
Jieyi Yi, Niansheng Tang
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Polygenic modeling with bayesian sparse linear mixed models. [PDF]
PLoS Genetics, 2013 Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies.
Xiang Zhou +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. Current boosting approaches also offer methods accounting for random effects and thus enable prediction ...
Griesbach, Colin +2 more
openaire +5 more sources
Journal of Plant Ecology, 2023 glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has rapidly gained recognition and popularity among ecologists ...
Jiangshan Lai +3 more
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An adjusted coefficient of determination (R2) for generalized linear mixed models in one go
Biometrical journal. Biometrische Zeitschrift, 2023 The coefficient of determination (R2) is a common measure of goodness of fit for linear models. Various proposals have been made for extension of this measure to generalized linear and mixed models.
H. Piepho
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CytoGLMM: conditional differential analysis for flow and mass cytometry experiments
BMC Bioinformatics, 2021 Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential ...
Christof Seiler +7 more
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Report Quality of Generalized Linear Mixed Models in Psychology: A Systematic Review
Frontiers in Psychology, 2021 Generalized linear mixed models (GLMMs) estimate fixed and random effects and are especially useful when the dependent variable is binary, ordinal, count or quantitative but not normally distributed.
Roser Bono, R. Alarcón, M. Blanca
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