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Statistical Matching Analysis for Complex Survey Data With Applications

Author

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  • Pier Luigi Conti
  • Daniela Marella
  • Mauro Scanu

Abstract

The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on the basis of a modification of the conditional independence assumption in the presence of logical constraints. The corresponding measure of uncertainty is estimated via sample data. Finally, a simulation study is performed, and an application to a real case is provided. Supplementary materials for this article are available online.

Suggested Citation

  • Pier Luigi Conti & Daniela Marella & Mauro Scanu, 2016. "Statistical Matching Analysis for Complex Survey Data With Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1715-1725, October.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:516:p:1715-1725
    DOI: 10.1080/01621459.2015.1112803
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    Citations

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    Cited by:

    1. Claramunt González, Juan & van Delden, Arnout & de Waal, Ton, 2023. "Assessment of the effect of constraints in a new multivariate mixed method for statistical matching," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    2. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    3. Christal Hamilton & Claire Altman & James Bachmeier & Cody Spence, 2022. "Legal status and health disparities: An examination of health insurance coverage among the foreign-born," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(16), pages 453-488.
    4. Chiara Elena Dalla & Menon Martina & Perali Federico, 2019. "An Integrated Database to Measure Living Standards," Journal of Official Statistics, Sciendo, vol. 35(3), pages 531-576, September.
    5. Francesco D. d’Ovidio & Paola Perchinunno & Laura Antonucci, 2021. "Data Integration Techniques for the Identification of Poverty Profiles," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 515-531, August.
    6. Gessendorfer Jonathan & Beste Jonas & Drechsler Jörg & Sakshaug Joseph W., 2018. "Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?," Journal of Official Statistics, Sciendo, vol. 34(4), pages 909-933, December.
    7. Andrea Cutillo & Mauro Scanu, 2020. "A Mixed Approach for Data Fusion of HBS and SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 411-437, July.
    8. Daniela Marella & Danny Pfeffermann, 2023. "Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching," International Statistical Review, International Statistical Institute, vol. 91(2), pages 269-293, August.
    9. Zahra Rezaei Ghahroodi, 2023. "Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1023-1051, September.
    10. Perchinunno, Paola & Mongelli, Lucia & d’Ovidio, Francesco D., 2020. "Statistical matching techniques in order to plan interventions on socioeconomic weakness: An Italian case," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).

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