An Exact Algorithm for the Stratification Problem with Proportional Allocation

TL;DR

This paper introduces an exact algorithm based on graph theory to optimally solve the statistical stratification problem with proportional allocation, reducing variance in survey sampling.

Contribution

The paper presents a novel exact algorithm for the stratification problem using minimal path concepts, improving precision over previous heuristic methods.

Findings

Algorithm effectively minimizes variance in real data scenarios.

Computational results demonstrate improved accuracy over existing methods.

Method applicable to large-scale stratification problems.

Abstract

We report a new optimal resolution for the statistical stratification problem under proportional sampling allocation among strata. Consider a finite population of N units, a random sample of n units selected from this population and a number L of strata. Thus, we have to define which units belong to each stratum so as to minimize the variance of a total estimator for one desired variable of interest in each stratum,and consequently reduce the overall variance for such quantity. In order to solve this problem, an exact algorithm based on the concept of minimal path in a graph is proposed and assessed. Computational results using real data from IBGE (Brazilian Central Statistical Office) are provided.