Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders. [PDF]
G3 (Bethesda), 2022 Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data.
Malik MA, Michoel T.
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The identification of mediating effects using genome-based restricted maximum likelihood estimation. [PDF]
PLoS Genetics, 2023 Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the ...
Cornelius A Rietveld +2 more
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Reducing computational demands of restricted maximum likelihood estimation with genomic relationship matrices [PDF]
Genetics Selection Evolution, 2023 Restricted maximum likelihood estimation of genetic parameters accounting for genomic relationships has been reported to impose computational burdens which typically are many times higher than those of corresponding analyses considering pedigree based ...
Karin Meyer
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Restricted maximum likelihood estimation for animal models using derivatives of the likelihood [PDF]
Genetics Selection Evolution, 1996 Cet article decrit une methode d'estimation du maximum de vraisemblance restreinte utilisant les derivees premiere et seconde de la vraisemblance. La methode est basee sur une procedure de differenciation automatique ne necessitant pas l'inversion de grandes matrices.
Smith SP, Meyer K
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Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters. [PDF]
PLoS ONE, 2013 Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maximum likelihood (REML) is computationally efficient for large data sets and complex linear mixed effects models.
Kaarina Matilainen +4 more
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Barekeng, 2022 The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure.
Vera Maya Santi +2 more
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Restricted maximum likelihood estimation of covariances in sparse linear models [PDF]
Genetics Selection Evolution, 1998 This paper surveys the theoretical and computational development of the restricted maximum likelihood (REML) approach for the estimation of covariance matrices in linear stochastic models. A new derivation of this approach is given, valid under very weak conditions on the noise. Then the calculation of the gradient of restricted loglikelihood functions
Groeneveld Eildert, Neumaier Arnold
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The Restricted EM Algorithm for Maximum Likelihood Estimation under Linear Restrictions on the Parameters [PDF]
Journal of the American Statistical Association, 1995 Abstract The EM algorithm is one of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems. But when the parameters must satisfy a set of linear restrictions, the EM algorithm may be too complicated to apply directly.
Dong K. Kim, Jeremy M. G. Taylor
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Evaluating the performance of Bayesian and restricted maximum likelihood estimation for stepped wedge cluster randomized trials with a small number of clusters [PDF]
BMC Medical Research Methodology, 2022 Background Stepped wedge trials are an appealing and potentially powerful cluster randomized trial design. However, they are frequently implemented with a small number of clusters.
Kelsey L. Grantham +4 more
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Estimating variances and covariances for multivariate animal models by restricted maximum likelihood [PDF]
Genetics Selection Evolution, 1991 Summary — Restricted maximum likelihood estimates of variance and covariance components can be obtained by direct maximization of the associated likelihood using standard, derivative-free optimization procedures. In general, this requires a multi-dimensional search and numerous evaluations of the (log) likelihood function.
Meyer K
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