Shrinkage Estimation Using Ranked Set Samples

The purpose of this article is two-fold. First, we consider the ranked set sampling (RSS) estimation and testing hypothesis for the parameter of interest (population mean). Then, we suggest some alternative estimation strategies for the mean parameter based on shrinkage and pretest principles. Generally speaking, the shrinkage and pretest methods use the non-sample information (NSI) regarding that parameter of interest. In practice, NSI is readily available in the form of a realistic conjecture based on the experimenter's knowledge and experience with the problem under consideration. It is advantageous to use NSI in the estimation process to construct improved estimation for the parameter of interest. In this contribution, the large sample properties of the suggested estimators will be assessed, both analytically and numerically. More importantly, a Monte Carlo simulation is conducted to investigate the relative performance of the estimators for moderate and large samples. For illustrative purposes, the proposed methodology is applied to a published data set.