57 research outputs found
A new method for the estimation of variance matrix with prescribed zeros in nonlinear mixed effects models
We propose a new method for the Maximum Likelihood Estimator (MLE) of
nonlinear mixed effects models when the variance matrix of Gaussian random
effects has a prescribed pattern of zeros (PPZ). The method consists in
coupling the recently developed Iterative Conditional Fitting (ICF) algorithm
with the Expectation Maximization (EM) algorithm. It provides positive definite
estimates for any sample size, and does not rely on any structural assumption
on the PPZ. It can be easily adapted to many versions of EM.Comment: Accepted for publication in Statistics and Computin
Sward structure of marandu palisadegrass subjected to continuous stocking and nitrogen-induced rhythms of growth
Estimation of (co)variance components and genetic parameters for weights of red-winged tinamou using random regression models
Avaliação nutricional da polpa de maçã como suplementação energética para bovinos
Modelos matemåticos utilizados para descrever curvas de crescimento em aves aplicados ao melhoramento genético animal
Growth increments and stability over time in fast-growing forest tree species
Zamudio, F. Facultad de Ciencias Forestales, Universidad de Talca, PO Box 747, 2 Norte 685, Talca, Chile.We review an analytical procedure useful for measuring the influence of time on the phenotypic expression
of cumulative growth. It consists of separating the genetic control into two meaningful components: the cumulative genetic
effect of growth increments and the effect of genotype Ă time interaction. The analysis is performed via a hierarchical
mixed linear model. Data from three Pinus tecunumanii Eguiluz & J.P. Perry provenanceâprogeny trials
established by the CAMCORE Cooperative in South America were used to assess the practical application of the
method. Results suggest that the simple observation of ageâage correlations for cumulative growth can be misleading,
because they hide the effects of family and within-family variation. The partition of cumulative growth into mutually
exclusive growth increments can help to discriminate covariance components, which are also useful to predict the degree
of association between growth at different ages and the potential value for early selection
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