Asymmetric Loss Functions and Sample Size Determination: A Bayesian Approach

In designing monitoring systems for public health tasks it can be important to give different weights to the cases of under- and overestimation of a binomial parameter. We show how asymmetric loss functions can be used for this aim. Bayesian interval-based approaches can be combined with these loss functions and with prior knowledge about diagnostic classification errors to determine optimal sample sizes.