Results 1 to 10 of about 1,975,717 (186)
On non-negative estimation of variance components in mixed linear models [PDF]
Journal of Advanced Research, 2016 Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced.
Heba A. El Leithy +2 more
doaj +2 more sources
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 desirable properties from a possibly very large set of candidate statistical models.
Müller, Samuel +2 more
openaire +5 more sources
Sparse probit linear mixed model [PDF]
Machine Learning, 2017 Published version, 21 pages, 6 ...
Stephan Mandt +5 more
openaire +3 more sources
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
openaire +5 more sources
, 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
+4 more sources
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
doaj +2 more sources
BLMM: Parallelised computing for big linear mixed models
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
doaj +1 more source
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
doaj +1 more source
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
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
doaj +1 more source