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A simulated annealing-based algorithm for selecting balanced samples

Author

Listed:
  • 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)

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.

Suggested Citation

  • Roberto Benedetti & Maria Michela Dickson & Giuseppe Espa & Francesco Pantalone & Federica Piersimoni, 2022. "A simulated annealing-based algorithm for selecting balanced samples," Computational Statistics, Springer, vol. 37(1), pages 491-505, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01113-3
    DOI: 10.1007/s00180-021-01113-3
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    References listed on IDEAS

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    1. Chauvet, Guillaume & Ruiz-Gazen, Anne, 2017. "A comparison of pivotal sampling and unequal probability sampling with replacement," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 1-5.
    2. Guillaume Chauvet & Yves Tillé, 2006. "A fast algorithm for balanced sampling," Computational Statistics, Springer, vol. 21(1), pages 53-62, March.
    3. Jean-Claude Deville & Yves Tille, 2004. "Efficient balanced sampling: The cube method," Biometrika, Biometrika Trust, vol. 91(4), pages 893-912, December.
    4. Alfio Marazzi & Yves Tillé, 2017. "Using past experience to optimize audit sampling design," Review of Quantitative Finance and Accounting, Springer, vol. 49(2), pages 435-462, August.
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