Spatial estimation approach under ranked set sampling from spatial correlated finite population.

Abstract

Ranked Set Sampling (RSS) is preferred over Simple Random Sampling (SRS), when measuring an observation is expensive or time consuming, but can be easily ranked at a negligible cost. While working with spatial population, classical statistical methods fail to capture the dependency present in the underlying data. In this article, an attempt was made to develop efficient estimation procedure through RSS sampling design incorporating spatial dependency among sampling units of a spatial finite population. Distance between spatial units was taken as measure of spatial dependency. The properties of the proposed Spatial Estimator (SE) were further studied empirically through a simulation study. The proposed Spatial Estimator (SE) under RSS of population mean from spatial data was found to be better than usual RSS estimator