Results 11 to 20 of about 1,982,495 (283)
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
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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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Multiple testing correction in linear mixed models. [PDF]
, 2016 BackgroundMultiple hypothesis testing is a major issue in genome-wide association studies (GWAS), which often analyze millions of markers. The permutation test is considered to be the gold standard in multiple testing correction as it accurately takes ...
Eskin, Eleazar +3 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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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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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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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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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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