Assessment of precision with aversity to overstatement
N. T. Longford
Abstract
The sampling variance and the standard error of an estimator
are usually estimated with small or no bias.
An argument is put forward that they should be overestimated,
to reflect our aversity to overstating the precision.
Such aversity is characterized by an asymmetric loss function.
The minimum-expected-loss estimators
of the sampling variances and standard errors
of parameters in ordinary regression are derived
for several classes of loss functions.
South African Journal of Statistics 47; to appear in 2013.
July 2012