Abstract
In this paper recent results about the application of Bayesian networks to official statistics are presented. Bayesian networks are multivariate statistical models able to represent and manage complex dependence structures. Here they are proposed as a useful and unique framework by which it is possible to deal with many problems typical of survey data analysis. In particular here we focus on categorical variables and show how to derive classes of contingency table estimators in case of stratified sampling designs. Having this technology poststratification, integration and missing data imputation become possible. Furthermore we briefly discuss how to use Bayesian networks for decision as a support system to monitor and manage the data production process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ballin, M., De Francisci, S., Scanu, M., Tininini, L., Vicard, P.: Integrated statistical systems: an approach to preserve coherence between a set of surveys based on the use of probabilistic expert systems. In: NTTS (New Techniques and Technologies for Statistics) seminar (2009) Available via DIALOG http://tinyurl.com/yzh3l28.Cited20February2009
Ballin, M., Scanu, M., Vicard, P.: Paradata and Bayesian networks: a tool for monitoring and troubleshooting the data production process. In: Working Paper n 66 - 2006. Dipartimento di Economia Università Roma Tre (2006) Available via DIALOG http://host.uniroma3.it/dipartimenti/economia/pdf/wp66.pdf.
Ballin, M., Scanu, M., Vicard, P.: Estimation of contingency tables in complex survey sampling using probabilistic expert systems. J. Statist. Plann. Inference 140, 1501–1512 (2010)
Ballin, M., Vicard, P.: A proposal for the use of graphical representation in official statistics. In: Proceeding of SCO2001 (Bressanone, 24-26 settembre 2001), pp. 487-492. CLEUP, Padova (2001)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer Verlag, Heidelberg (1999)
Di Zio, M., Sacco, G., Scanu, M., Vicard, P.: Multivariate techniques for imputation based on Bayesian networks. Neural Network World 4, 303–309 (2005)
Di Zio, M., Sacco, G., Scanu, M., Vicard, P.: Metodologia e software per l’imputazione di dati mancanti tramite le reti bayesiane. In: Liseo, B., Montanari, G. E. and Torelli, N. (eds.) Software Pioneers, pp. 307-321. Franco Angeli, Milano (2006)
Di Zio, M., Scanu, M., Coppola, L., Luzi, O., Ponti, A.: Bayesian Networks for Imputation. J. Roy. Statist. Soc./A 167, 2, 309–322 (2004)
Groves, R.M., Heeringa, S.G.: Responsive design for household surveys: tools for actively controlling survey errors and costs. J. Roy. Statist. Soc./A, 169, 3, 439–457 (2006)
Holt, D., Smith, T.M.F.: Post Stratification. J. Roy. Statist. Soc./A, 142, 33–46 (1979)
Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)
Lauritzen, S.L.: Graphical Models. Oxford University Press, Oxford (1996)
Lauritzen, S.L., Nilsson, D.: Representing and Solving Decision Problems with Limited Information. Management Science, 47, 1235–1251 (2001)
Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall, Upper Saddle River (2004)
Pfeffermann, D.: The role of sampling weights when modelling survey data. International Statistical Review 61, 317–337 (1993)
Rao, J.N.K., Wu.: Empirical Likelihood Methods. In: Rao, J.N.K. (eds.) Handbook of Statisics, Volume 29, Sample Surveys: Theory, Methods and Inference, pp. 189-207. Elsevier, Amsterdam (2010)
Thibaudeau, Y., Winkler, W.E. : Bayesian networks representations, generalized imputation, and synthetic micro-data satisfying analytic constraints. In: Research Report RRS2002/9 - 2002. U.S. Bureau of the Census (2002) Available via DIALOG www.census.gov/srd/papers/pdf/rrs2002-09.pdf
Acknowledgements
Thanks are due to Marco Ballin, Marco Di Zio and Giuseppe Sacco, coauthors of most of the citations in this paper. The research was supported by MIUR/PRIN 2007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vicard, P., Scanu, M. (2012). Applications of Bayesian Networks in Official Statistics. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-21037-2_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21036-5
Online ISBN: 978-3-642-21037-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)