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Population empirical likelihood estimation in dual frame surveys

Author

Listed:
  • Maria Mar Rueda

    (University of Granada)

  • Maria Giovanna Ranalli

    (University of Perugia)

  • Antonio Arcos

    (University of Granada)

  • David Molina

    (University of Granada)

Abstract

Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960‘s, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (Stat Sin 24:335–355, 2014) and using both the dual and the single frame approach. The extension of the proposed methodology to more than two frame surveys is also sketched. The performance of the proposed estimators in terms of relative bias and relative mean squared error is tested through simulation experiments. These experiments indicate that the proposed estimators yield better results than other likelihood-based estimators proposed in the literature.

Suggested Citation

  • Maria Mar Rueda & Maria Giovanna Ranalli & Antonio Arcos & David Molina, 2021. "Population empirical likelihood estimation in dual frame surveys," Statistical Papers, Springer, vol. 62(5), pages 2473-2490, October.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:5:d:10.1007_s00362-020-01200-5
    DOI: 10.1007/s00362-020-01200-5
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    References listed on IDEAS

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    1. Yves G. Berger & Ewa Kabzińska, 2020. "Empirical Likelihood Approach for Aligning Information from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 54-74, April.
    2. Maria del Mar Rueda & Antonio Arcos & David Molina & Maria Giovanna Ranalli, 2018. "Estimation Techniques for Ordinal Data in Multiple Frame Surveys with Complex Sampling Designs," International Statistical Review, International Statistical Institute, vol. 86(1), pages 51-67, April.
    3. A. Arcos & M. Rueda & M. Martínez-Miranda, 2005. "Using multiparametric auxiliary information at the estimation stage," Statistical Papers, Springer, vol. 46(3), pages 339-358, July.
    4. 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.
    5. 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:

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    2. 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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