IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v25y2020i2d10.1007_s13253-020-00390-7.html
   My bibliography  Save this article

Estimation in Complex Sampling Designs Based on Resampling Methods

Author

Listed:
  • Bardia Panahbehagh

    (Kharazmi University)

Abstract

Generally, to select a representative sample of the population, we use a combination of several probabilistic sampling methods which is called a complex sampling design. A complex sampling design usually needs very sophisticated mathematical calculations to provide unbiased estimators of the population parameters. Therefore, only a limited number of sampling designs are commonly used in practice. In the present study, to overcome this complexity, we propose a general method of estimation based on resampling that is suitable for all standard designs, either conventional or adaptive. In this method, we calculate Murthy estimator as an unbiased estimator for the population mean and its variance estimator without intensive mathematical calculations. Using this method, researchers can perform any probability design with the guarantee that the estimator is unbiased. To show this ability and as an application of the method, we introduce Adaptive Random Walk Sampling as a complex and efficient sampling design, proper for the quadrat-based environmental population. Despite the complexity of this design, the method proposed in this paper provides unbiased estimator for the population mean based on the design and then makes it a practical design. Simulations confirm the expected performance of the method.

Suggested Citation

  • Bardia Panahbehagh, 2020. "Estimation in Complex Sampling Designs Based on Resampling Methods," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 206-228, June.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:2:d:10.1007_s13253-020-00390-7
    DOI: 10.1007/s13253-020-00390-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-020-00390-7
    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/s13253-020-00390-7?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. Lorenzo Fattorini, 2006. "Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities," Biometrika, Biometrika Trust, vol. 93(2), pages 269-278, June.
    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. 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.
    2. Lorenzo Fattorini & Timothy G. Gregoire & Sara Trentini, 2018. "The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 358-373, September.
    3. L.-C. Zhang & M. Patone, 2017. "Graph sampling," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 277-299, December.
    4. Sara Franceschi & Rosa Maria Di Biase & Agnese Marcelli & Lorenzo Fattorini, 2022. "Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations," Stats, MDPI, vol. 5(2), pages 1-16, April.
    5. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org.
    6. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    7. Grafström Anton & Matei Alina, 2015. "Coordination of Conditional Poisson Samples," Journal of Official Statistics, Sciendo, vol. 31(4), pages 649-672, December.
    8. Jiajin Wei & Ping He & Tiejun Tong, 2024. "Estimating the Reciprocal of a Binomial Proportion," International Statistical Review, International Statistical Institute, vol. 92(1), pages 1-16, April.
    9. Lorenzo Fattorini, 2009. "An adaptive algorithm for estimating inclusion probabilities and performing the Horvitz–Thompson criterion in complex designs," Computational Statistics, Springer, vol. 24(4), pages 623-639, December.
    10. Alessandro Chiarucci & Rosa Maria Di Biase & Lorenzo Fattorini & Marzia Marcheselli & Caterina Pisani, 2017. "Joining the Incompatible: Exploiting Floristic Lists for the Sample-based Estimation of Species Richness," Department of Economics University of Siena 753, Department of Economics, University of Siena.
    11. 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.
    12. Lorenzo Fattorini & Alberto Meriggi & Enrico Merli & Paolo Varuzza, 2020. "Sampling Strategies to Estimate Deer Density by Drive Counts," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 168-185, June.
    13. Tomasz Bąk, 2014. "Triangular Method of Spatial Sampling," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(1), pages 9-22, January.
    14. Luis Sanguiao Sande & Li-Chun Zhang, 2021. "Design-Unbiased Statistical Learning in Survey Sampling," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 714-744, August.
    15. ak Tomasz B, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.

    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:jagbes:v:25:y:2020:i:2:d:10.1007_s13253-020-00390-7. 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.