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Bayesian networks for imputation

Author

Listed:
  • Marco Di Zio
  • Mauro Scanu
  • Lucia Coppola
  • Orietta Luzi
  • Alessandra Ponti

Abstract

Summary. Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables (statistical consistency) and preservation of logical constraints in data (logical consistency). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.

Suggested Citation

  • Marco Di Zio & Mauro Scanu & Lucia Coppola & Orietta Luzi & Alessandra Ponti, 2004. "Bayesian networks for imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 309-322, May.
    • Handle: RePEc:bla:jorssa:v:167:y:2004:i:2:p:309-322
    DOI: 10.1046/j.1467-985X.2003.00736.x
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    References listed on IDEAS

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    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    2. A. P. Dawid & J. Mortera & V. L. Pascali & D. Van Boxel, 2002. "Probabilistic Expert Systems for Forensic Inference from Genetic Markers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 577-595, December.
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    2. Rosa Aghdam & Mojtaba Ganjali & Parisa Niloofar & Changiz Eslahchi, 2016. "Inferring gene regulatory networks by an order independent algorithm using incomplete data sets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 893-913, April.
    3. Pilar Rey del Castillo & Jaime Villanueva-Garcia, 2018. "Linking Tax Morale and Personal Income Tax in Spain," CESifo Working Paper Series 7218, CESifo.
    4. Daniela Marella & Paola Vicard, 2022. "Bayesian network structural learning from complex survey data: a resampling based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 981-1013, October.
    5. Coutinho Wieger & Waal Ton de & Shlomo Natalie, 2013. "Calibrated Hot-Deck Donor Imputation Subject to Edit Restrictions," Journal of Official Statistics, Sciendo, vol. 29(2), pages 299-321, September.
    6. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
    7. Rancoita, Paola M.V. & Zaffalon, Marco & Zucca, Emanuele & Bertoni, Francesco & de Campos, Cassio P., 2016. "Bayesian network data imputation with application to survival tree analysis," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 373-387.
    8. Daniela Marella, 2018. "Pc Complex: Pc Algorithm For Complex Survey Data," Departmental Working Papers of Economics - University 'Roma Tre' 0240, Department of Economics - University Roma Tre.

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