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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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Linear quantile mixed models [PDF]
Statistics and Computing, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
GERACI M, BOTTAI M
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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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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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Sparse probit linear mixed model [PDF]
Machine Learning, 2017 Published version, 21 pages, 6 ...
Stephan Mandt +5 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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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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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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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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Admissibility of Continuous Unbiased Estimators in Linear Mixed Models [PDF]
The Egyptian Statistical Journal, 1991 For a given linear function of the fixed effects in the usual mixed linear model, within the class of estimators, a discontinuous unbiased estimator is introduced.
Samia El-arishy
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