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The Use of Uncertainty to Choose Matching Variables in Statistical Matching

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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

Statistical matching aims at combining information available in distinct sample surveys referred to the same target population. The matching is usually based on a set of common variables shared by the available data sources. For matching purposes just a subset of all the common variables should be used, the so called matching variables. The paper presents a novel method for selecting the matching variables based on the analysis of the uncertainty characterizing the matching framework. The uncertainty is caused by unavailability of data for estimating parameters describing the association/correlation between variables not jointly observed in a single data source. The paper focuses on the case of categorical variables and presents a sequential procedure for identifying the most effective subset of common variables in reducing the overall uncertainty.

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References

  1. Agresti A (2013) Categorical data analysis, 3rd edn. Wiley, New York

    MATH  Google Scholar 

  2. Agresti A, Yang MC (1987) An empirical investigation of some effects of sparseness in contingency tables. Comput Stat Data Anal 5:9–21

    Article  MATH  Google Scholar 

  3. Bishop YM, Fienberg SE, Holland PW (1975) Discrete Multivariate Analysis: Theory and Practice. MIT. Press, Cambridge, MA. Paperback edition

    Google Scholar 

  4. Cohen ML (1991) Statistical matching and microsimulation models. In: Citro, H (ed) Improving information for social policy decisions: The uses of microsimulation modeling, vol II Technical papers, Washington D.C

    Google Scholar 

  5. Conti PL, Marella D, Scanu M (2012) Uncertainty analysis in statistical matching. J Official Stat 28:69–88

    Google Scholar 

  6. D’Orazio M, Di Zio M, Scanu M (2006) Statistical matching: theory and practice. Wiley, Chichester

    Book  MATH  Google Scholar 

  7. D’Orazio M (2016) StatMatch: statistical matching (aka data fusion). R package version 1.2.4 http://CRAN.R-project.org/package=StatMatch

  8. Särndal CE, Swensson B, Wretman J (1992) Model assisted survey sampling. Springer, New York

    Book  MATH  Google Scholar 

  9. Vantaggi B (2008) Statistical matching of multiple sources: a look through coherence. In J Approximate Reasoning 49:701–711

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Istat, via Cesare Balbo 16, 00184, Roma, Italy

    Marcello D’Orazio, Marco Di Zio & Mauro Scanu

Authors
  1. Marcello D’Orazio
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  2. Marco Di Zio
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  3. Mauro Scanu
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Corresponding author

Correspondence to Marco Di Zio .

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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D’Orazio, M., Di Zio, M., Scanu, M. (2017). The Use of Uncertainty to Choose Matching Variables in Statistical Matching. 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_19

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_19

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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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