Results 11 to 20 of about 2,001,852 (330)
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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Subset selection for linear mixed models. [PDF]
Biometrics, 2023 AbstractLinear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covariates—while accounting for this structured dependence—remains a challenge. We introduce a Bayesian decision analysis for subset selection with LMMs. Using a Mahalanobis
Kowal DR.
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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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, 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
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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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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 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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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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