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Bayesian Inference for a Finite Population Total Using Linked Data

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Soft Methods for Data Science (SMPS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

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Abstract

We consider the problem of estimating the total (or the mean) of a continuous variable in a finite population setting, using the auxiliary information provided by a covariate which is available in a different file. However the matching steps between the two files is uncertain due to a lack of identification code for the single unit. We propose a fully Bayesian approach which merges the record linkage step with the subsequent estimation procedure.

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References

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Authors and Affiliations

  1. MEMOTEF, Sapienza Università di Roma, Rome, Italy

    Dario Briscolini, Brunero Liseo & Andrea Tancredi

Authors
  1. Dario Briscolini
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  2. Brunero Liseo
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  3. Andrea Tancredi
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Corresponding author

Correspondence to Andrea Tancredi .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  2. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  3. Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  4. Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  5. Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  6. Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  7. Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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Briscolini, D., Liseo, B., Tancredi, A. (2017). Bayesian Inference for a Finite Population Total Using Linked Data. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

  • eBook Packages: EngineeringEngineering (R0)

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