IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v129y2017icp50-57.html
   My bibliography  Save this article

Exact optimal sample allocation: More efficient than Neyman

Author

Listed:
  • Wright, Tommy

Abstract

Neyman allocation (1) rarely yields integer solutions; (2) does not guarantee minimum sampling variance after rounding; and (3) can result in a stratum sample size that exceeds the overall stratum size. Using a simple decomposition, our exact optimal algorithms completely prevent these problems.

Suggested Citation

  • Wright, Tommy, 2017. "Exact optimal sample allocation: More efficient than Neyman," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 50-57.
    • Handle: RePEc:eee:stapro:v:129:y:2017:i:c:p:50-57
    DOI: 10.1016/j.spl.2017.04.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715217301657
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2017.04.026?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. Tommy Wright, 2012. "The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives," The American Statistician, Taylor & Francis Journals, vol. 66(4), pages 217-224, November.
    2. Carrizosa, Emilio & Romero Morales, Dolores, 2007. "A biobjective method for sample allocation in stratified sampling," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1074-1089, March.
    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. M. G. M. Khan & Jacek Wesołowski, 2019. "Neyman-type sample allocation for domains-efficient estimation in multistage sampling," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 563-592, December.
    2. Bryan E. Shepherd & Kyunghee Han & Tong Chen & Aihua Bian & Shannon Pugh & Stephany N. Duda & Thomas Lumley & William J. Heerman & Pamela A. Shaw, 2023. "Multiwave validation sampling for error‐prone electronic health records," Biometrics, The International Biometric Society, vol. 79(3), pages 2649-2663, September.
    3. Wright, Tommy, 2020. "A general exact optimal sample allocation algorithm: With bounded cost and bounded sample sizes," Statistics & Probability Letters, Elsevier, vol. 165(C).
    4. Wesołowski Jacek, 2019. "Multi-Domain Neyman-Tchuprov Optimal Allocation," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 1-12, December.
    5. Jacek Wesołowski, 2019. "Multi-Domain Neyman-Tchuprov Optimal Allocation," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 1-12, December.

    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. Carrizosa, Emilio, 2010. "Unequal probability sampling from a finite population: A multicriteria approach," European Journal of Operational Research, Elsevier, vol. 201(2), pages 500-504, March.
    2. Friedrich, Ulf & Münnich, Ralf & de Vries, Sven & Wagner, Matthias, 2015. "Fast integer-valued algorithms for optimal allocations under constraints in stratified sampling," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 1-12.
    3. Wright, Tommy, 2020. "A general exact optimal sample allocation algorithm: With bounded cost and bounded sample sizes," Statistics & Probability Letters, Elsevier, vol. 165(C).
    4. Hasler, Caren & Tillé, Yves, 2014. "Fast balanced sampling for highly stratified population," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 81-94.

    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:eee:stapro:v:129:y:2017:i:c:p:50-57. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.