Robust inference under r-size-biased sampling without replacement from finite population.

ABSTRACT: The case of size-biased sampling of known order from a finite population without replacement is considered. The behavior of such a sampling scheme is studied with respect to the sampling fraction. Based on a simulation study, it is concluded that such a sample cannot be treated either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution and as the sampling fraction increases, the biasness in the sample decreases resulting in a transition from an r-size-biased sample to a random sample. A modified version of a likelihood-free method is adopted for making statistical inference for the unknown population parameters, as well as for the size of the population when it is unknown. A simulation study, which takes under considerat

SUBMITTER: Economou P 

PROVIDER: S-EPMC9041951 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature