3 Variances of indices in stratified samples 5 3.1 How the FES is sampled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Indices in stratified samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.3 Variances of indices in stratified samples . . . . . . . . . . . . . . . . . . . . . . 7 3.4 Variances of indices in IID samples . . . . . . . . . . . . . . . . . ...

Correct ranking of animals in across-country genetic evaluation requires careful design of both, the model for breeding value estimation and the adjustment for heterogeneous variance. The Nordic across-country genetic evaluation for yield traits of Danish, Finnish and Swedish red dairy cattle is based on a multiple-trait random regression test-day model, which considers observations from differ...

Most existing hashing methods adopt some projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit (zero or one) by thresholding. Typically, the variances of different projected dimensions are different for existing projection functions such as principal component analysis (PCA). Using the same...

Comparing the Statistical Tests for Homogeneity of Variances by Zhiqiang Mu Testing the homogeneity of variances is an important problem in many applications since statistical methods of frequent use, such as ANOVA, assume equal variances for two or more groups of data. However, testing the equality of variances is a difficult problem due to the fact that many of the tests are not robust agains...

Small area estimation based on area level models typically assumes that sampling error variances for the direct survey small area estimates are known. In practice we use estimates of the sampling error variances, and these can contain substantial error. This suggests modeling the sampling variances to improve them and to quantify e , dects of their estimation error on small area inferences. We ...

Gaussian kernels with flexible variances provide a rich family of Mercer kernels for learning algorithms. We show that the union of the unit balls of reproducing kernel Hilbert spaces generated by Gaussian kernels with flexible variances is a uniform Glivenko-Cantelli (uGC) class. This result confirms a conjecture concerning learnability of Gaussian kernels and verifies the uniform convergence ...