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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Restricted Type II Maximum Likelihood Priors on Regression Coefficients [PDF]
Bayesian Analysis, 2020 In Bayesian hypothesis testing and model selection, prior distributions must be chosen carefully. For example, setting arbitrarily large prior scales for location parameters, which is common practice in estimation problems, can lead to undesirable behavior in testing (Lindley's paradox).
Peña Pizarro, Víctor, Berger, James O.
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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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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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Performa Restricted Maximum Likelihood and Maximum Likelihood Estimators on Small Area Estimation [PDF]
Journal of Physics: Conference Series, 2018 In this paper, it was studied performance of maximum likelihood and restricted maximum likelihood method to estimate random factor on small area estimation. To evaluate of their performances, it was used mean square error as criteria. Based on data simulation, it was found that mean square error of restricted maximum likelihood estimator smaller than ...
Muhammad Nusrang +3 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.
Jeremy M G Taylor
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Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies
Journal of Research of the National Institute of Standards and Technology, 2011 A formulation of the problem of combining data from several sources is discussed in terms of random effects models. The unknown measurement precision is assumed not to be the same for all methods. We investigate maximum likelihood solutions in this model.
Rukhin, Andrew L.
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