Abstract
Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.
Highlights
Balanced sampling refers to a class of techniques aimed at randomly selecting units from a given population
Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population
A new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem
Summary
Balanced sampling refers to a class of techniques aimed at randomly selecting units from a given population. A general solution for balanced sampling was proposed by Deville and Tillé (2004), whose cube method allows for the selection of balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variables with fast execution time (Chauvet and Tillé 2006; Grafström and Lisic 2016). This method is based on a random transformation of the inclusion probabilities vector to draw a sample that exactly, or at least approximately, satisfies the original inclusion probabilities and balancing equations (Tillé 2011).
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