Recursive Neyman Algorithm for Optimum Sample Allocation under Box Constraints on Sample Sizes in Strata

TL;DR

This paper introduces RNABOX, a recursive algorithm for optimal sample allocation in stratified sampling with both lower and upper bounds on sample sizes, extending classical Neyman allocation methods.

Contribution

It develops a new recursive algorithm, RNABOX, for optimal sample allocation under box constraints, generalizing classical Neyman allocation techniques.

Findings

RNABOX efficiently solves constrained allocation problems.

The algorithm is implemented in R within the stratallo package.

Optimality conditions are derived using KKT conditions.

Abstract

The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified $\pi$ estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository.