IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v29y2014i5p1345-1363.html
   My bibliography  Save this article

A new resampling method for sampling designs without replacement: the doubled half bootstrap

Author

Listed:
  • Erika Antal
  • Yves Tillé

Abstract

A new and very fast method of bootstrap for sampling without replacement from a finite population is proposed. This method can be used to estimate the variance in sampling with unequal inclusion probabilities and does not require artificial populations or utilization of bootstrap weights. The bootstrap samples are directly selected from the original sample. The bootstrap procedure contains two steps: in the first step, units are selected once with Poisson sampling using the same inclusion probabilities as the original design. In the second step, amongst the non-selected units, half of the units are randomly selected twice. This procedure enables us to efficiently estimate the variance. A set of simulations show the advantages of this new resampling method. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Erika Antal & Yves Tillé, 2014. "A new resampling method for sampling designs without replacement: the doubled half bootstrap," Computational Statistics, Springer, vol. 29(5), pages 1345-1363, October.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1345-1363
    DOI: 10.1007/s00180-014-0495-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-014-0495-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-014-0495-0?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. Jean‐François Beaumont & Zdenek Patak, 2012. "On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling," International Statistical Review, International Statistical Institute, vol. 80(1), pages 127-148, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu Mengxuan & Landsman Victoria & Graubard Barry I., 2021. "Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1059-1078, December.
    2. J. A. Mayor-Gallego & J. L. Moreno-Rebollo & M. D. Jiménez-Gamero, 2019. "Estimation of the finite population distribution function using a global penalized calibration method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 1-35, March.
    3. Żądło Tomasz, 2021. "On the generalisation of Quatember’s bootstrap," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 163-178, March.
    4. Tomasz Żądło, 2021. "On the generalisation of Quatember's bootstrap," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 163-178, March.
    5. María del Mar Rueda & Beatriz Cobo & Antonio Arcos, 2021. "Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables," Mathematics, MDPI, vol. 9(6), pages 1-13, March.

    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. Wayne A. Fuller & Jason C. Legg & Yang Li, 2017. "Bootstrap Variance Estimation for Rejective Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1562-1570, October.
    2. Zhonglei Wang & Liuhua Peng & Jae Kwang Kim, 2022. "Bootstrap inference for the finite population mean under complex sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1150-1174, September.
    3. Sixia Chen & David Haziza & Zeinab Mashreghi, 2022. "A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs," Stats, MDPI, vol. 5(2), pages 1-17, June.
    4. Marie-Hélène Felt & David Laferrière, 2020. "Sample Calibration of the Online CFM Survey," Technical Reports 118, Bank of Canada.
    5. Pier Luigi Conti & Fulvia Mecatti, 2022. "Resampling under Complex Sampling Designs: Roots, Development and the Way Forward," Stats, MDPI, vol. 5(1), pages 1-12, March.
    6. Xu Mengxuan & Landsman Victoria & Graubard Barry I., 2021. "Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1059-1078, December.
    7. Jean-François Beaumont & Nelson Émond, 2022. "A Bootstrap Variance Estimation Method for Multistage Sampling and Two-Phase Sampling When Poisson Sampling Is Used at the Second Phase," Stats, MDPI, vol. 5(2), pages 1-19, March.
    8. Żądło Tomasz, 2021. "On the generalisation of Quatember’s bootstrap," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 163-178, March.
    9. Beaumont, Jean-François & Charest, Anne-Sophie, 2012. "Bootstrap variance estimation with survey data when estimating model parameters," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4450-4461.
    10. Jean-Francois Beaumont & J. N. K. Rao, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1071-1076, December.
    11. Marius Stefan & Michael A. Hidiroglou, 2023. "A Bootstrap Variance Procedure for the Generalised Regression Estimator," International Statistical Review, International Statistical Institute, vol. 91(2), pages 294-317, August.
    12. Heng Chen & Rallye Shen, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Calibration for Single-Location Retailers," Technical Reports 109, Bank of Canada.
    13. Daniela Marella & Paola Vicard, 2022. "Bayesian network structural learning from complex survey data: a resampling based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 981-1013, October.
    14. Tomasz Żądło, 2021. "On the generalisation of Quatember's bootstrap," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 163-178, March.

    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:29:y:2014:i:5:p:1345-1363. 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.