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On the Sampling Size for Inverse Sampling

Author

Listed:
  • Daniele Cuntrera

    (Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy
    These authors contributed equally to this work.)

  • Vincenzo Falco

    (Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy
    These authors contributed equally to this work.)

  • Ornella Giambalvo

    (Department of Business, Economics, and Statistics, University of Palermo, Viale delle Scienze, Building 13, 90128 Palermo, Sicily, Italy
    These authors contributed equally to this work.)

Abstract

In the Big Data era, sampling remains a central theme. This paper investigates the characteristics of inverse sampling on two different datasets (real and simulated) to determine when big data become too small for inverse sampling to be used and to examine the impact of the sampling rate of the subsamples. We find that the method, using the appropriate subsample size for both the mean and proportion parameters, performs well with a smaller dataset than big data through the simulation study and real-data application. Different settings related to the selection bias severity are considered during the simulation study and real application.

Suggested Citation

  • Daniele Cuntrera & Vincenzo Falco & Ornella Giambalvo, 2022. "On the Sampling Size for Inverse Sampling," Stats, MDPI, vol. 5(4), pages 1-15, November.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:4:p:67-1144:d:973450
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    References listed on IDEAS

    as
    1. Masayuki Henmi & Ryo Yoshida & Shinto Eguchi, 2007. "Importance Sampling Via the Estimated Sampler," Biometrika, Biometrika Trust, vol. 94(4), pages 985-991.
    2. Khyati Ahlawat & Anuradha Chug & Amit Prakash Singh, 2019. "Benchmarking framework for class imbalance problem using novel sampling approach for big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 824-835, August.
    3. Jae Kwang Kim & Zhonglei Wang, 2019. "Sampling Techniques for Big Data Analysis," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 177-191, May.
    4. Vincenzo G. Genova & Michele Tumminello & Fabio Aiello & Massimo Attanasio, 2021. "A network analysis of student mobility patterns from high school to master’s," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1445-1464, December.
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