Enhanced Cube Implementation For Highly Stratified Population

TL;DR

This paper introduces a new sampling method for highly stratified populations that enhances existing designs by reducing variance and addressing computational challenges related to inclusion probabilities.

Contribution

It proposes an improved balanced sampling technique tailored for highly stratified populations, overcoming limitations of traditional methods.

Findings

Significantly reduces variance of estimators.

Decreases computational complexity in sample selection.

Enhances efficiency of stratified sampling processes.

Abstract

A balanced sampling design should always be the adopted strategies if auxiliary information is available. Besides, integrating a stratified structure of the population in the sampling process can considerably reduce the variance of the estimators. We propose here a new method to handle the selection of a balanced sample in a highly stratified population. The method improves substantially the commonly used sampling design and reduces the time-consuming problem that could arise if inclusion probabilities within strata do not sum to an integer.