In combining several tests of significance the individual test statistics are allowed to be dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this...

In a general variance component model with positive variance components a short-cut method is presented that yields almost everywhere for these components positive estimators that are invariant with respect to mean value translation and stay near the unbiasedness.

In combining several tests of significance the individual test statistics are allowed to be dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this...

In a general variance component model with positive variance components a short-cut method is presented that yields almost everywhere for these components positive estimators that are invariant with respect to mean value translation and stay near the unbiasedness.

In combining several tests of significance the individual test statistics are allowed to be dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this...

In a general variance component model with positive variance components a short-cut method is presented that yields almost everywhere for these components positive estimators that are invariant with respect to mean value translation and stay near the unbiasedness.