IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v37y2022i1d10.1007_s00180-021-01113-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-021-01113-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-021-01113-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raphaël Jauslin & Bardia Panahbehagh & Yves Tillé, 2022. "Sequential spatially balanced sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. R. Benedetti & M. S. Andreano & F. Piersimoni, 2019. "Sample selection when a multivariate set of size measures is available," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 1-25, March.
    3. Maria Michela Dickson & Yves Tillé, 2016. "Ordered spatial sampling by means of the traveling salesman problem," Computational Statistics, Springer, vol. 31(4), pages 1359-1372, December.
    4. Chauvet, Guillaume & Do Paco, Wilfried, 2018. "Exact balanced random imputation for sample survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 1-16.
    5. Guillaume Chauvet & Ronan Le Gleut, 2021. "Inference under pivotal sampling: Properties, variance estimation, and application to tesselation for spatial sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 108-131, March.
    6. Hervé Cardot & Camelia Goga & Pauline Lardin, 2014. "Variance Estimation and Asymptotic Confidence Bands for the Mean Estimator of Sampled Functional Data with High Entropy Unequal Probability Sampling Designs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 516-534, June.
    7. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
    8. Kaeding, Matthias, 2016. "Fast, approximate MCMC for Bayesian analysis of large data sets: A design based approach," Ruhr Economic Papers 660, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Roberto Benedetti & Federica Piersimoni & Paolo Postiglione, 2017. "Spatially Balanced Sampling: A Review and A Reappraisal," International Statistical Review, International Statistical Institute, vol. 85(3), pages 439-454, December.
    10. Hasler, Caren & Tillé, Yves, 2014. "Fast balanced sampling for highly stratified population," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 81-94.
    11. Leuenberger, Michael & Eustache, Esther & Jauslin, Raphaël & Tillé, Yves, 2022. "Balancing a sample almost perfectly," Statistics & Probability Letters, Elsevier, vol. 180(C).
    12. Tomasz Bąk, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    13. Matei Alina, 2021. "Book Review," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1079-1081, December.
    14. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    15. Louis‐Paul Rivest & Serigne Abib Gaye, 2023. "Using Survey Sampling Algorithms For Exact Inference in Logistic Regression," International Statistical Review, International Statistical Institute, vol. 91(1), pages 18-34, April.
    16. Lennart Bondesson, 2010. "Conditional and Restricted Pareto Sampling: Two New Methods for Unequal Probability Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 514-530, September.
    17. Tillé, Yves & Favre, Anne-Catherine, 2005. "Optimal allocation in balanced sampling," Statistics & Probability Letters, Elsevier, vol. 74(1), pages 31-37, August.
    18. Yves Tillé, 2016. "The legacy of Corrado Gini in survey sampling and inequality theory," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 167-176, August.
    19. G. Alleva & G. Arbia & P. D. Falorsi & V. Nardelli & A. Zuliani, 2023. "Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 983-999, September.
    20. Stefan Zins & Jan Pablo Burgard, 2020. "Planning Domain Sizes in Cluster Sampling," Research Papers in Economics 2020-06, University of Trier, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01113-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.