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We develop a model for the estimation of the variance of sensitive variable in randomized response procedure, when ranked set sampling (RSS) design is used under ranking criteria.
These six sub-samples were used for the estimation of the variance components in the model discussed below.
Cross-classified models allow for the estimation of the variance at each level, taking into account the complex hierarchical structure.
If the causal variants are not in linkage disequilibrium with the SNPs used for the estimation of the variance components, their contribution to the variance will not be captured.
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The key step toward a test statistic for the GPI is the estimation of the variance of GPI.
SMVar improves the estimation of the variance for small number of replicates like in this study.
For the estimation of the conditional variance from which the VaR is computed, univariate and multivariate GARCH models are used.
For the estimation of the marginal variance of noisy observations, in [5] the following relation is proposed: (9).
Variance components for the estimation of intra-individual variance and the inter-individual variance were done and the results presented by the coefficient of variation (CV) and the intra-class correlation coefficients (ICC).
The choice of optimal designs for the estimation of variance components depends on the method of estimation, the model used, and the values of the variance components themselves.
In the following theorem, we give an inequality for the estimation of variance of the k-gamma random variable.
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