Results 11 to 20 of about 4,151,630 (320)
Linear quantile mixed models [PDF]
Statistics and Computing, 2011 Dependent data arise in many studies. For example, children with the same parents or living in neighbouring geographic areas tend to be more alike in many characteristics than individuals chosen at random from the population at large; observations taken ...
Bottai, M, Geraci, M
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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 ...
Müller, Samuel +2 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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rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models
Methods in Ecology and Evolution, 2017 Martin A Stoffel +2 more
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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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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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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
semanticscholar +1 more source
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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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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Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
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