scholarly journals The restricted maximum likelihood method for variance estimation in a mixed model with additive penalized-spline

2019 ◽  
Vol 1321 ◽  
pp. 022060
Author(s):  
A N R Chytrasari ◽  
S H Kartiko ◽  
Danardono
Keyword(s):  
Maximum Likelihood ◽  
Mixed Model ◽  
Maximum Likelihood Method ◽  
Variance Estimation ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Penalized Spline

scholarly journals Paramètres génétiques et phénotypiques des performances de reproduction des brebis des races Boujaâd et Sardi au Maroc

2006 ◽  
Vol 59 (1-4) ◽  
pp. 51 ◽  
Author(s):  
I. Boujenane ◽  
A. Chikhi
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Beni Mellal

L’étude a porté sur l’analyse de 1 264 et 811 performances de reproduction de brebis respectivement de races Boujaâd et Sardi. Ces données ont été collectées de 1993-94 à 1999-2000 dans le domaine expérimental Déroua de l’Institut national de la recherche agronomique de Béni Mellal. Les paramètres génétiques des caractères de reproduction ont été estimés par la méthode Reml (restricted maximum likelihood method) d’estimation des composantes de la variance et de la covariance. Les répétabilités estimées chez la race Boujaâd ont été de 0,18 et 0,17 respectivement pour la taille de portée à la naissance et au sevrage, de 0,23 et 0,18 respectivement pour le poids de portée à la naissance et au sevrage, et de 0,18 pour la durée de gravidité. Chez la race Sardi, les estimations correspondantes ont été respectivement de 0,21 et 0,18, 0,24 et 0,15, et 0,16. Les héritabilités estimées chez la race Boujaâd ont été de 0,18 et 0,11 respectivement pour la taille de portée à la naissance et au sevrage, de 0,18 et 0,11 respectivement pour le poids de portée à la naissance et au sevrage, et de 0,04 pour la durée de gravidité. Les estimations correspondantes chez la race Sardi ont été respectivement de 0,21 et 0,18, 0,24 et 0,15, et 0,16. Les corrélations génétiques et phénotypiques entre ces caractères ont varié respectivement de 0,83 à 1,00 et de 0,27 à 0,93 chez la race Boujaâd, et de 0,06 à 0,96 et de 0,07 à 0,82 chez la race Sardi. Il a été conclu que ces paramètres pourraient être utilisés dans des programmes de sélection pour améliorer la productivité des brebis des races Boujaâd et Sardi.

scholarly journals Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders

2020 ◽  
Author(s):  
Muhammad Ammar Malik ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Maximum Likelihood ◽  
Latent Variables ◽  
Variance Components ◽  
Maximum Likelihood Method ◽  
Likelihood Function ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Confounding Factors ◽  
Gradient Based

AbstractLinear mixed modelling is a popular approach for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in linear mixed models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting solution is poorly characterized and the efficiency of the method may be suboptimal. Here we prove analytically that maximumlikelihood latent variables can always be chosen orthogonal to the known confounding factors, in other words, that maximum-likelihood latent variables explain sample covariances not already explained by known factors. Based on this result we propose a restricted maximum-likelihood method which estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors, and show that this reduces to probabilistic PCA on that subspace. The method then estimates the variance-covariance parameters by maximizing the remaining terms in the likelihood function given the latent variables, using a newly derived analytic solution for this problem. Compared to gradient-based optimizers, our method attains equal or higher likelihood values, can be computed using standard matrix operations, results in latent factors that don’t overlap with any known factors, and has a runtime reduced by several orders of magnitude. We anticipate that the restricted maximum-likelihood method will facilitate the application of linear mixed modelling strategies for learning latent variance components to much larger gene expression datasets than currently possible.

Characteristics and heritability analysis of head scales of the Hungarian meadow viper (Vipera ursinii rakosiensis, Méhely 1893)

2012 ◽  
Vol 33 (3-4) ◽  
pp. 393-400 ◽  
Author(s):  
Bálint Üveges ◽  
Bálint Halpern ◽  
Tamás Péchy ◽  
János Posta ◽  
István Komlósi
Keyword(s):  
Animal Model ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Fixed Effects ◽  
Maximum Likelihood Method ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Covariance Components ◽  
Heritability Analysis ◽  
Variance And Covariance Components

The objective of our research was to determine the heritability of head scale numbers of Vipera ursinii rakosiensis. 430 specimens (177 males and 253 females) were included in the analysis, most of which were born and raised in the Hungarian Meadow Viper Conservation Centre between 2004 and 2008. Due to the controlled breeding conditions, the dams of the offspring were known, and the sires were known in 51% of the cases. Only the ancestors of the wild caught specimens were unknown, but these animals were included as parents in the analysis. Photographic identification was used to identify and characterise the specimens, the majority over consecutive years. We counted the following scales: loreal-, circumocular-, apical-, and crown (intercanthal- and intersupraocular-) shields, as well as presence-absence data of other characteristics which are detailed further in the article. The variance and covariance components were determined via the restricted maximum likelihood method. The repeatability animal model consisted of the year of birth and the sex of the snakes as fixed effects, the dam as permanent environmental, and the animal as random effects. Heritability values varied between 0.32 and 0.70. We also report scale numbers and statistics of differences between scale numbers of sexes.

scholarly journals Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders

2021 ◽  
Author(s):  
Muhammad Ammar Malik ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Latent Variables ◽  
Variance Components ◽  
Maximum Likelihood Method ◽  
Likelihood Function ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Confounding Factors

Abstract Random effects models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in random effects models is a challenge that has so far relied on numerical gradient-based optimizers to maximize the likelihood function. This is unsatisfactory because the resulting solution is poorly characterized and the efficiency of the method may be suboptimal. Here we prove analytically that maximum-likelihood latent variables can always be chosen orthogonal to the known confounding factors, in other words, that maximum-likelihood latent variables explain sample covariances not already explained by known factors. Based on this result we propose a restricted maximum-likelihood method which estimates the latent variables by maximizing the likelihood on the restricted subspace orthogonal to the known confounding factors, and show that this reduces to probabilistic PCA on that subspace. The method then estimates the variance-covariance parameters by maximizing the remaining terms in the likelihood function given the latent variables, using a newly derived analytic solution for this problem. Compared to gradient-based optimizers, our method attains greater or equal likelihood values, can be computed using standard matrix operations, results in latent factors that don’t overlap with any known factors, and has a runtime reduced by several orders of magnitude. Hence the restricted maximum-likelihood method facilitates the application of random effects modelling strategies for learning latent variance components to much larger gene expression datasets than possible with current methods.

scholarly journals Some Remarks on Restricted Panel Data Model

2017 ◽  
Vol 13 (2) ◽  
pp. 7168-7175
Author(s):  
Saja Yaseen Abdulsamad ◽  
Ameera Jaber Mohaisen
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Data Model ◽  
Maximum Likelihood Method ◽  
Linear Constraints ◽  
Restricted Maximum Likelihood ◽  
Likelihood Method ◽  
Panel Data Model

In this paper , we investigate some remarks on panel data model with linear constraints on the coefficients of the random panel data model. Furthermore, it investigates the inferences . The restricted maximum likelihood method is employed to making inferences on the random panel data model.

Sign in / Sign up

Export Citation Format

Share Document