24-26 September 2012 UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Use of Machine Learning Methods to Impute Categorical Data Pilar Rey del Castillo* EUROSTAT, Unit B1: Quality, Research and Methodology

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Use of Machine Learning Methods to Impute Categorical Data September 2012  Problem non-response in statistical surveys missing information in machine learning different approaches evaluation criteria Aim: show the commitment to the almost exclusive use of probabilistic data models prevents statisticians from using the most convenient technologies Case of categorical variables: practical recommendations from the statistical approach just reuse procedures designed for numeric variables

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Outline of the presentation September Review non-response treatments  imputation procedures: evaluation criteria 2.Recommendations for categorical data imputation from the statistical community: why these are not appropriate 3.Results of comparisons with two machine learning methods 4.Final remarks

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Non-response treatments September 2012 Deletion procedures: using only the units with complete data for further analysis Tolerance procedures: internal, not removing incomplete records or completing them Imputation procedures: replacing each missing value by an estimate

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Imputation procedures September 2012 Algorithmic methods: use an algorithm to produce the imputations (cold and hot-deck, nearest-neighbour, mean, machine learning classification & prediction techniques…) Model-based methods: the predictive distributions have a formal statistical model  state of the art: MI

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Criteria for evaluating the imputation results September 2012 Statistical surveys: valid & efficient inferences, being treatment part of the overall procedure "… Judging the quality of missing data procedures by their ability to recreate the individual missing values (according to hit-rate, mean square error, etc.) does not lead to choosing procedures that result in valid inference, which is our objective" (Rubin, 1996) Machine learning: general artificial intelligence framework (empirical results through simulating missing data and measuring the closeness between real & imputed)

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Categorical data imputation in statistical surveys September 2012 State of the art: MI or other model-based  Log-linear model : not always possible Logistic regression models: sometimes problems at the estimation step Binary case: Rubin & Schenker (1986), Schafer (1997): to approximate by using a Gaussian distribution Non-binary case: Yucel & Zaslavsky (2003), Van Gingel et al. (2007): rounding multivariate normal distribution Criticisms from the practical perspective (Horton (2003), Ake (2005), Allison (2006), Demirtas (2008)) Contradiction (theoretical framework: focus on model adequacy)  (practical recommendations: models clearly not adequate)

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Problem of categorical data imputation to be solved September 2012 Survey microdata file: opinion poll (no.2750 in CIS catalogue) ‒ Quantitative variables (8): ideological self-location; rating of three specific political figures; likelihood to vote; likelihood to vote for three specific political parties… ‒ Ordered categorical variables (2): government and opposition party ratings (converted to quantitative) ‒ Categorical variables with non-ordered categories (7): voting intention; voting memory; the autonomous community; the political party the respondent would prefer to see win… Voting intention to be imputed: 11 categories (biggest political parties, "blank vote", "abstention", "others") interviews with no missing values

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Imputation methods to be compared September 2012 MI logistic regression Classifiers (matching each class with one of the Voting intention categories) ‒ Fuzzy min-max neural network classifier recently extended to deal with mixed numeric & categorical data as inputs (Rey del Castillo & Cardeñosa, 2012) ‒ Bayesian network classifier: not Naïve Bayes classifier but a more complex architecture learnt with a score + search paradigm

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Comparison criterion September 2012 Not possible classical surveys inference criterion because no models EUREDIT project: Wald statistic for categorical variables: but none of the methods overcome the proposed test! Correctly imputed rate is used (ten-fold cross-validation)

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Results of the comparison September 2012 Imputation method Correctly imputed rate % MI logistic regression 66.0 Fuzzy min-max neural network classifier 86.1 Bayesian network classifier 87.4

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Conclusions & final remarks September Always similar differences between machine learning / MI logistic 2.Simplest case with missing data exclusively on one variable 3.Extensible to numeric variables ? 4.Machine learning procedures easier to automate Non-dependence on model assumptions Don't break down when large number of variables ? More robust to outliers ? 5.Machine learning may be used for massive imputation tasks

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing Thank you !!! September 2012

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing References (1) September 2012 Ake, C. F. (2005), Rounding After Multiple Imputation with Non-Binary Categorical Covariates, SAS Conference Proceedings: SAS User Group International 30, Philadelphia, PA, April Allison, P. (2006), Multiple Imputation of Categorical Variables under the Multivariate Normal Model, paper presented at the Annual Meeting of the American Sociological Association, Montreal Convention Center, Montreal, Quebec, Canada, August Demirtas, H. (2008), On Imputing Continuous Data When the Eventual Interest Pertains to Ordinalized Outcomes Via Threshold Concept, Computational Statistics and Data Analysis, vol. 52, pp Horton, N. J., Lipsitz, S. R. and Parzen, M. (2003), A Potential for Bias when Rounding in Multiple Imputation, The American Statistician, vol. 57, no. 4, pp , November Rey-del-Castillo, P., and Cardeñosa, J. (2012), Fuzzy Min–Max Neural Networks for Categorical Data: Application to Missing Data Imputation, Neural Computing and Applications, vol. 21, no. 6 (2012), pp , DOI /s00521 ‐ 011 ‐ 0574 ‐ x, Springer-Verlag London. Rubin, D. B. (1996), Multiple Imputation After 18+ Years, Journal of the American Statistical Association, vol. 91, no. 434, Applications and Case Studies, June 1996.

UNECE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing References (2) September 2012 Rubin, D. B. and Schenker, N. (1986), Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse, Journal of the American Statistical Association, vol. 81, no. 394, Survey Research Methods, June Schafer, J. L. and Graham, J. W. (2002), Missing Data: Our View of the State of the Art, Psychological Methods, vol. 7, no. 2, pp Van Ginkel, J. R., Van der Ark, L. A. and Sijtsma, K. (2007), Multiple Imputation of Item Scores when Test Data are Factorially Complex, British Journal of Mathematics and Statistical Psychology, vol. 60, pp Yucel, R. M. and Zaslavsky, A. M. (2003), Practical Suggestions on Rounding in Multiple Imputation, Proceedings of the Joint American Statistical Association Meeting, Section on Survey Research Methods, Toronto, Canada, August 2003.