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Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities

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  • Lorenzo Fattorini

Abstract

A modification of the Horvitz-Thompson estimator is proposed for complex sampling designs. The inclusion probabilities are estimated by means of independent replications of the sampling scheme. The properties of the resulting estimator are derived. Guidelines for choosing the appropriate number of replications are given and some applications are considered. Copyright 2006, Oxford University Press.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:269-278
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    File URL: http://hdl.handle.net/10.1093/biomet/93.2.269
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. L.-C. Zhang & M. Patone, 2017. "Graph sampling," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 277-299, December.
    6. 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.
    7. Haoge Chang, 2023. "Design-based Estimation Theory for Complex Experiments," Papers 2311.06891, arXiv.org.
    8. 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.
    9. Grafström Anton & Matei Alina, 2015. "Coordination of Conditional Poisson Samples," Journal of Official Statistics, Sciendo, vol. 31(4), pages 649-672, December.
    10. 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.
    11. 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.
    12. 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.
    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. 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.
    15. 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.

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