A simulated annealing-based algorithm for selecting balanced samples

Roberto Benedetti, Maria Michela Dickson (), Giuseppe Espa, Francesco Pantalone and Federica Piersimoni
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Roberto Benedetti: University ”G. d’Annunzio” of Chieti-Pescara
Maria Michela Dickson: University of Trento
Giuseppe Espa: University of Trento
Francesco Pantalone: University of Perugia
Federica Piersimoni: Directorate for Methodology and Statistical Process Design

Computational Statistics, 2022, vol. 37, issue 1, No 21, 505 pages

Abstract: 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.

Keywords: Balanced sampling; Auxiliary variables; Sampling algorithms; Simulated annealing (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01113-3

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