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Kernel-based methods for combining information of several frame surveys

Author

Listed:
  • I. Sánchez-Borrego

    (University of Granada)

  • A. Arcos

    (University of Granada)

  • M. Rueda

    (University of Granada)

Abstract

A sample selected from a single sampling frame may not represent adequatly the entire population. Multiple frame surveys are becoming increasingly used and popular among statistical agencies and private organizations, in particular in situations where several sampling frames may provide better coverage or can reduce sampling costs for estimating population quantities of interest. Auxiliary information available at the population level is often categorical in nature, so that incorporating categorical and continuous information can improve the efficiency of the method of estimation. Nonparametric regression methods represent a widely used and flexible estimation approach in the survey context. We propose a kernel regression estimator for dual frame surveys that can handle both continuous and categorical data. This methodology is extended to multiple frame surveys. We derive theoretical properties of the proposed methods and numerical experiments indicate that the proposed estimator perform well in practical settings under different scenarios.

Suggested Citation

  • I. Sánchez-Borrego & A. Arcos & M. Rueda, 2019. "Kernel-based methods for combining information of several frame surveys," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 71-86, January.
    • Handle: RePEc:spr:metrik:v:82:y:2019:i:1:d:10.1007_s00184-018-0686-8
    DOI: 10.1007/s00184-018-0686-8
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    References listed on IDEAS

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    1. Montanari, Giorgio E. & Ranalli, M. Giovanna, 2005. "Nonparametric Model Calibration Estimation in Survey Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1429-1442, December.
    2. M. Rueda & I. Sánchez-Borrego, 2009. "A predictive estimator of finite population mean using nonparametric regression," Computational Statistics, Springer, vol. 24(1), pages 1-14, February.
    3. Rao, J. N. K. & Wu, Changbao, 2010. "Pseudo–Empirical Likelihood Inference for Multiple Frame Surveys," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1494-1503.
    4. Lohr, Sharon & Rao, J.N.K., 2006. "Estimation in Multiple-Frame Surveys," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1019-1030, September.
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    Cited by:

    1. Daniela Cocchi & Lorenzo Marchi & Riccardo Ievoli, 2022. "Bayesian Bootstrap in Multiple Frames," Stats, MDPI, vol. 5(2), pages 1-11, June.

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