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 many applications we obtain test statistics by combining estimates from different experiments or studies. The usual combined estimator of the overall effect in independent studies leads to systematic overestimates of the significance level, see Li et al. (1994). This results in a great number...

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 many applications we obtain test statistics by combining estimates from different experiments or studies. The usual combined estimator of the overall effect in independent studies leads to systematic overestimates of the significance level, see Li et al. (1994). This results in a great number...

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 many applications we obtain test statistics by combining estimates from different experiments or studies. The usual combined estimator of the overall effect in independent studies leads to systematic overestimates of the significance level, see Li et al. (1994). This results in a great number...