A common task in the analysis of multi-environmental trials (MET) by linear mixed models (LMM) is estimation variance components (VCs). Most often, MET data are imbalanced (e.g., due to selection). The imbalance mechanism can be missing completely at random (MCAR), (MAR), or not random. If missing-data pattern was caused selection, it usually MAR. In this case, likelihood-based methods preferre...

Use of variance-component estimation for mapping of quantitative-trait loci in humans is a subject of great current interest. When only trait values, not genotypic information, are considered, variance-component estimation can also be used to estimate heritability of a quantitative trait. Inbred pedigrees present special challenges for variance-component estimation. First, there are more varian...

inheritance of resistance to stripe rust was studied in wheat by determining the responses of 5 hexaploid wheat cultivars and 10 f1 progenies of their diallel mating crosses, against 2 races of pathogen including 174e174a+, 134e134 a+ in completely randomized design with 3 repetitions under greenhouse conditions at tehran university.the components of resistance including latent period, infectio...

We represent the Brownian model for continuous traits with phylogenetic dependence as a variance components model, allowing for the novel estimation of edge weights for every branch of a tree graph. We show that maximum likelihood estimation carries an inherent bias often shrinking internal edges to zero, which is troublesome because this estimate is intended to compare covariance structures as...

The purpose of this review is to define possible breeding objectives for Southern African beef cattle farmers and to review different means of expressing genetic reproductive merit. The breeding objective considered was to maximize the number of calves born or weaned for a given number of cows in a herd under prevailing environmental and management conditions. This is a complex trait that has m...

Noise variance estimation is required in many image denoising, compression, and segmentation applications. In this work, we propose a fast noise variance estimation algorithm based on principal component analysis of image blocks. First, we rearrange image blocks into vectors and compute the covariance matrix of these vectors. Then, we use Bartlett’s test in order to select the covariance matrix...

1 Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, United States, 2 Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States, 3 Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Arizona, Tucson, AZ, United States, 4 Division of Pulmonary, Critical Care, Sleep, and Occ...

A procedure is derived for computing standard errors in random intercept models for estimates obtained from the EM algorithm. We discuss two different approaches: a Gauu-Hermite quadrature for Gaussian random eeect models and a nonparametric maximum likelihood estimation for an unspec-iied random eeect distribution. An approximation of the expected Fisher information matrix is proposed which is...

This paper analyzes the Gibbs sampler applied to a standard variance component model, and considers the question of how many iterations are required for convergence. It is proved that for K location parameters, with J observations each, the number of iterations required for convergence (for large K and J) is a constant times 1 + log K log J. This is one of the rst rigorous, a priori results abo...