Sentence examples for approaches for variance from inspiring English sources

The random regression representation of MACE facilitates exploitation of principal component or factor analytic approaches for variance component estimation and prediction of breeding values for international sire evaluations.

We first ran a combined data analysis by a linear mixed model approach for variance component estimation and a jackknife approach for statistical test.

The objectives of this study are to evaluate the utility of the factor analytic approach for variance component estimation for MACE and to assess the impact of alternative parameterizations, both PC and FA, for practical prediction of breeding values with MACE.

The two commonly used approaches for producing variance estimates for estimated regression coefficients are, the analytical technique [ 9, 10] and a replication approach [ 9, 11].

We therefore use influence-based methods, a common approach for linearized variance estimation of survey statistics [ 18], to derive variances for the lexpit model's risk estimates.

Approach for calculating variance within lines derived and wrote by JAW in the appendix.

In the Additional file 1 we summarize the optimization algorithm and the influence approach for obtaining variance estimates for the lexpit model parameters.

We consider two approaches for estimating the variance under the null hypothesis.

The third approach that is considered for variance component estimation for MACE is the direct factor analytic approach that will be presented in an upcoming paper.

Expected value and mean-variance approaches do not predict preference for variance and positive skewness.

In this paper we describe four novel approaches to modeling heterogeneity variance - two novel model structures, and two approaches for use of moderately informative variance priors.