Abstract
In this paper we propose to use the object-oriented Bayesian network (OOBN) architecture to model measurement errors. We then apply our model to the Italian survey on household income and wealth (SHIW) 2008. Attention is focused on errors caused by the respondents. The parameters of the error model are estimated using a validation sample. The network is used to stochastically impute micro data for households. In particular imputation is performed also using an auxiliary variable. Indices are calculated to evaluate the performance of the correction procedure and show that accounting for auxiliary information improves the results. Finally, potentialities and possible extensions of the Bayesian network approach both to the measurement error context and to official statistics problems in general are discussed.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ballin, M., Scanu, M., Vicard, P.: Paradata and Bayesian networks: A tool for monitoring and troubleshooting the data production process. In: Working Paper no 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. Stat. Plan. Inference 140, 1501–1512 (2010)
Biemer, P.P.: Measurement errors in sample survey. In: Pfeffermann, D., Rao, C.R. (eds.) Handbook of Statistics, vol. 29A. Sample Surveys: Design, Methods and Applications. North-Holland, Amsterdam (2009)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks, 1st edn. Springer Publishing Company, Incorporated (2007)
Di Zio, M., Scanu, M., Coppola, L., Luzi, O., Ponti, A.: Bayesian networks for imputation. J. R. Stat. Soc. A 167(2), 309–322 (2004)
Di Zio, M., Sacco, G., Scanu, M., Vicard, P.: Multivariate techniques for imputation based on Bayesian networks. Neural Netw. World 4, 303–309 (2005)
Fuller, W.A.: Measurement Error Models. Wiley, New York (1987)
Hansen, M.H., Hurwitz, W.N., Bershad, M.A.: Measurement errors in censuses and surveys. Bull. Int. Stat. Inst. 38, 359–374 (1961)
Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence, pp. 302–313 (1997)
Lessler, J.T.: Measurement errors in surveys. In: Turner, C.F., Martin, E. (eds.) Surveying Subjective Phenomena, vol. 2, pp. 405–440. Russel Sage Foundation, New York (1984)
Liu, H., Setiono, R.: Chi2: Feature selection and discretization of numeric attributes. In: Proceedings of The Seventh International Conference with Artificial Intelligence (1995)
Mahalanobis, P.C.: Recent experiments in statistical sampling in the Indian Statisticla Institute. J. R. Stat. Soc. 109, 325–370 (1946)
Marella, D., Vicard, P.: Object-oriented Bayesian network for modeling the repsondent measurement error. Commun. Stat. Theory Methods 42:19, 3463–3477 (2013)
Neri, A., Ranalli, M.G.: To misreport or not to report? The case of the Italian survey on household income and wealth. Stat. Transit. 12, 281–300 (2011)
Shenoy, P.P.: Inference in hybrid Bayesian networks using mixtures of Gaussians. In: Proocedings of the 22nd Conference in Uncertainty in Artificial Intelligence (2006)
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.
Vicard, P., Dawid, A.P.: A statistical treatment of biases affecting the estimation of mutation rates. Mutat. Res. 547, 19–33 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Marella, D., Vicard, P. (2014). Modelling Measurement Errors by Object-Oriented Bayesian Networks: An Application to 2008 SHIW. In: Mecatti, F., Conti, P., Ranalli, M. (eds) Contributions to Sampling Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-05320-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-05320-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05319-6
Online ISBN: 978-3-319-05320-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)