Abstract
A criterion of optimality in prediction is proposed that requires the predictor to assume the same type of values as the random variable it is predicting. In the case of categorical responses, the method leads to the Bayesian classifier with a uniform prior. However, the method extends to other cases, such as zero-inflated observations, as well. The method, called best look-alike prediction (BLAP), justifies an “usual practice” from a theoretical standpoint. Application of BLAP to small area estimation is considered. A real-data example is discussed.