Not AvailableIn robust elementwise estimation approach, the variance components are expressed as functions of variances and covariances of sample covariance matrices in half-sib data structures. These variances and covariances were replaced by robust variances and covariances that were estimated in an elementwise manner. From the robust covariance matrices, we get robust estimates of variance components and heritability. Among the non-iterative robust estimators, biweight estimator with c=10 showed superior performance as compared to others. In the presence of outliers none of the classical methods give the reliable estimate, but the robust methods behave better than the classical estimators in terms of bias and MSE. Even in the presence of...
International audienceIn this paper, robust mean and covariance matrix estimation are considered in ...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
The assumption of equal variance in the normal regression model is not always appropriate. Cook and...
Variance components and functions thereof are important in many fields such as industry, agriculture...
The linear regression model requires robust estimation of parameters, if the measured data are conta...
Variance components estimation originated with estimating error variance in analysis of variance by ...
A severe limitation for the application of robust position and scale estimators having a high breakd...
Vita.The problem of estimating variance components in the random and mixed linear models has no sati...
Common methods for estimating variance components in Linear Mixed Models include Maximum Likelihood ...
Consideration is given to obtaining maximum-likelihood estimates of variance components for both bal...
Abstract Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive m...
Not AvailableModified ANOVA method, which enforces restriction on the parameter space of the varianc...
The purpose of this paper is to introduce some recent developments in variance component estimation ...
In this paper, we propose a new componentwise estimator of a dispersion matrix, based on a highly ro...
An ill-posed problem which involves heterogonous data can yield good results if the weight of observ...
International audienceIn this paper, robust mean and covariance matrix estimation are considered in ...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
The assumption of equal variance in the normal regression model is not always appropriate. Cook and...
Variance components and functions thereof are important in many fields such as industry, agriculture...
The linear regression model requires robust estimation of parameters, if the measured data are conta...
Variance components estimation originated with estimating error variance in analysis of variance by ...
A severe limitation for the application of robust position and scale estimators having a high breakd...
Vita.The problem of estimating variance components in the random and mixed linear models has no sati...
Common methods for estimating variance components in Linear Mixed Models include Maximum Likelihood ...
Consideration is given to obtaining maximum-likelihood estimates of variance components for both bal...
Abstract Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive m...
Not AvailableModified ANOVA method, which enforces restriction on the parameter space of the varianc...
The purpose of this paper is to introduce some recent developments in variance component estimation ...
In this paper, we propose a new componentwise estimator of a dispersion matrix, based on a highly ro...
An ill-posed problem which involves heterogonous data can yield good results if the weight of observ...
International audienceIn this paper, robust mean and covariance matrix estimation are considered in ...
This article discusses estimates of variance for two-stage models. We present the sandwich estimate ...
The assumption of equal variance in the normal regression model is not always appropriate. Cook and...