In order to estimate genetic parameters for production traits and reproductive performance, 115 465 records of production traits and 90 942 records of reproductive performance from Iranian Holstein cows that were collected during 1980 to 2004 at Animal Breeding Center of Iran, were used. The estimations were performed using Restricted Maximum Likelihood method (REML) under an animal model by DF...

INTRODUCTION Estimation of covariance components by Restricted Maximum Likelihood (REML) fitting an animal model is widely carried out using a derivative-free (DF) algorithm to locate the maximum of the likelihood function. While such algorithms have proven robust and easy to use, they are generally slow to converge, often requiring many likelihood evaluations, in particular for multivariate an...

In the statistical analysis of fMRI data, the parameter of primary interest is the effect of a contrast; of secondary interest is its standard error, and of tertiary interest is the standard error of this standard error, or equivalently, the degrees of freedom (df). In a ReML (Restricted Maximum Likelihood) analysis, we show how spatial smoothing of temporal autocorrelations increases the effec...

This paper considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REML-scoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a Levenberg-Marquardt restricted step modification which ensures that the REML-likelihood increases at each iteration. ...

Abstract The objective of this work was to compare different methods estimate heritability in 30 pigeon pea families from the F3, F4, F5, and F6 generations, for nine variables. experimental design a randomized complete block with three replicates 20 plants per plot. Broad-sense estimated by analysis variance ( A N O V A) [ h 2b-E(MS)], restricted maximum likelihood/best linear unbiased predict...

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. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type ...

The purpose of this study is to present guidelines in selection of statistical and computing algorithms for variance components estimation when computing involves software packages. For this purpose two major methods are to be considered: residual maximal likelihood (REML) and Bayesian via Gibbs sampling. Expectation-Maximization (EM) REML is regarded as a very stable algorithm that is able to ...

This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of varia...