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Statistical Models for Data Analysis pp 73–80Cite as

Correction of Incoherences in Statistical Matching

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Abstract

Statistical matching is studied inside a coherent setting, by focusing on the problem of removing inconsistencies. When structural zeros among involved variables are present, incoherencies on the parameter estimations can arise. The aim is to compare different methods to remove such incoherences based on specific pseudo-distances. The comparison is given through an exemplifying example of 100 simulations from a known population with three categorical variables, that carries out to the light peculiarities of the statistical matching problem.

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References

  • Biazzo, V., & Gilio, A. (2005). Some theoretical properties of conditional probability assessments. Lecture Notes in Computer Science LNAI, 3571, 775–787.

    Article  Google Scholar 

  • Capotorti, A., Regoli, G., & Vattari, F. (2010). Correction of incoherent conditional probability assessments. International Journal of Approximate Reasoning, 51(6), 718–727.

    Article  MathSciNet  MATH  Google Scholar 

  • Capotorti, A., & Vantaggi, B. (2011). In Proc. of 7th int. symp. on imprecise probability: theories and applications - ISIPTA’11. Incoherence correction strategies in statistical matching (pp. 109–1118). Innsbruck (Austria).

    Google Scholar 

  • Coletti, G., & Scozzafava, R. (2002). Probabilistic logic in a coherent setting, Series “Trends in Logic”. Dordrecht: Kluwer Academic.

    Google Scholar 

  • de Finetti, B. (1972). Sull’impostazione assiomatica del calcolo delle probabilità. Probability, induction, statistics. London: Wiley.

    Google Scholar 

  • D’Orazio, M., Di Zio, M., & Scanu, M. (2006). Statistical matching for categorical data: displaying uncertainty and using logical constraints. Journal of Official Statistics, 22, 137–157.

    Google Scholar 

  • D’Orazio, M., Di Zio, M., & Scanu, M. (2006). Statistical matching: theory and practice. London: Wiley.

    Book  Google Scholar 

  • Kadane, J. B. (2001). Some statistical problems in merging data files. Journal of Official Statistics, 17, 423–433.

    Google Scholar 

  • Kulback, S. (1957). Information theory and statistics. New York: Wiley.

    Google Scholar 

  • Lindley, D. V., Tversky, A., & Brown, R. V. (1979). On the reconciliation of probability assessments. Journal of Royal Statistical Society A, 142(2), 146–180.

    Article  MathSciNet  MATH  Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (1983). On jointly estimating parameters and missing data by maximising the complete-data likelihood. The American Statistician, 37, 218–220.

    Google Scholar 

  • Paass, G. (1986). In G. H. Orcutt & H. Quinke (Eds.) Microanalytic simulation models to support social and financial policy. Statistical match: evaluation of existing procedures and improvements by using additional information (pp. 401–422). Amsterdam: Elsevier Science.

    Google Scholar 

  • Rässler, S. (2002). Statistical matching: a frequentist theory, practical applications and alternative Bayesian approaches. Lecture notes in statistics. New York: Springer.

    Google Scholar 

  • Rubin, D. B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business & Economic Statistics, 2(1), 87–94.

    Google Scholar 

  • Vantaggi, B. (2008). Statistical matching of multiple sources: A look through coherence. International Journal of Approximate Reasoning, 49(3), 701–711.

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Dip. Matematica e Informatica, Universitá di Perugia, Perugia, Italy

    Andrea Capotorti

  2. Dip. Scienze di Base e Applicate per l’Ingegneria, Universitá La Sapienza, Roma, Italy

    Barbara Vantaggi

Authors
  1. Andrea Capotorti
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  2. Barbara Vantaggi
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Corresponding author

Correspondence to Andrea Capotorti .

Editor information

Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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© 2013 Springer International Publishing Switzerland

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Capotorti, A., Vantaggi, B. (2013). Correction of Incoherences in Statistical Matching. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_9

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