BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Razzak Humera, Heumann Christian
Title
Hybrid Multiple Imputation in a Large Scale Complex Survey
Source
Statistics in Transition, 2019, vol. 20, nr 4, s. 33-58, rys., tab., bibliogr. s. 30-32
Keyword
Metodologia badań statystycznych
Methodology of statistical surveys
Note
summ.
Abstract
Large-scale complex surveys typically contain a large number of variables measured on an even larger number of respondents. Missing data is a common problem in such surveys. Since usually most of the variables in a survey are categorical, multiple imputation requires robust methods for modelling high-dimensional categorical data distributions. This paper introduces the 3-stage Hybrid Multiple Imputation (HMI) approach, computationally efficient and easy to implement, to impute complex survey data sets that contain both continuous and categorical variables. The proposed HMI approach involves the application of sequential regression MI techniques to impute the continuous variables by using information from the categorical variables, already imputed by a non-parametric Bayesian MI approach. The proposed approach seems to be a good alternative to the existing approaches, frequently yielding lower root mean square errors, empirical standard errors and standard errors than the others. The HMI method has proven to be markedly superior to the existing MI methods in terms of computational efficiency. The authors illustrate repeated sampling properties of the hybrid approach using simulated data. The results are also illustrated by child data from the multiple indicator survey (MICS) in Punjab 2014. (original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
Full text
Show
Bibliography
Show
  1. ANDERSON, A. B., BASILEVSKY, A., HUM, D. P., (1983). Missing data: A review of the literature. In J. D. W. P. H. Rossi and A. B. Anderson (Eds.), Handbook of survey research, New York: Academic Press.
  2. ARNOLD, B. C., PRESS, S. J., (1989). Compatible Conditional Distributions. Journal of the American Statistical Association, 84, pp. 152-156.
  3. ALLISON, P. D., (2000). Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research, 28, pp. 301-309.
  4. AKE, C. F., (2005). Rounding after multiple imputation with non-binary categorical covariates (paper 112-30). In Proceedings of the Thirteenth Annual SAS Users Group International Conference, SAS Institute Inc., Cary, NC, pp. 1-11.
  5. ANDRIDGE, R. R. (2009). Statistical methods for missing data in complex sample surveys. PhD thesis, The University of Michigan.
  6. AKMATOV, M. K., (2011). Child abuse in 28 developing and transitional countries--results from the Multiple Indicator Cluster Surveys, Int J Epidemiol, 40(1), pp. 219-27.
  7. ANKAIAH, N., RAVI, V., (2011). A novel soft computing hybrid for data imputation, Proceedings of the 7th international conference on data mining (DMIN), Las Vegas, USA.
  8. AZIM, S., AGGARWAL, S. (2014). Hybrid model for data imputation: using fuzzy c means and multi layer perceptron. Advance Computing Conference (IACC), 2014 IEEE International. IEEE, pp. 1281-1285.
  9. AUDIGIER, V., HUSSON, F., JOSSE, J., (2016). A principal component method to impute missing values for mixed data, Advances in Data Analysis and Classification, 10(1), pp. 5-26.
  10. AKANDE, O., LI, F., REITER, J., (2017). An empirical comparison of multiple imputation methods for categorical data, Amer. Statist, 71, pp. 162-170.
  11. ARMINA, R., ZAIN, A.M., ALI, N.A., SALLEHUDDIN, R., (2017). A review on missing value estimation using imputation algorithm, Journal of Physics: Conference Series, 892, pp. 012004.
  12. AUDIGIER, V., WHITE, I. R., JOLANI, S., DEBRAY, T., QUARTAGNO, M., CARPENTER, J., ESCHE-RIGON, M., (2017a), Multiple imputation for multilevel data with continuous and binary variables, arXiv preprint, arXiv:1702.00971.
  13. AUDIGIER, V., HUSSON, F., JOSSE, J., (2017b). MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing, 27, pp. 501-518.
  14. BREIMAN, L., (2001). Random Forests. Machine Learning, 45(1), pp. 5-32.
  15. BERNAARDS, C. A., BELIN, T. R., SCHAFER, J. L., (2007). Robustness of a multivariate normal approximation for imputation of binary incomplete data, Statistics in Medicine, 26, pp. 1368-1382.
  16. BURGETTE, L. F., REITER, J. P., (2010). Multiple Imputation for Missing Data via Sequential Regression Trees. American Journal of Epidemiology, Oxford University Press, 172(9), pp. 1070-6.
  17. CHIB, S., HAMILTON, B. H., (2002). Semiparametric Bayes analysis of longitudinal data treatment models, Journal of Econometrics, 110, pp. 67-89.
  18. CAPPA, C., KHAN, S.M., (2011). Understanding caregivers' attitudes towards physical punishment of children: evidence from 34 low- and middle-income countries, Child Abuse Negl, 35(12), pp. 1009-21.
  19. DUNSON, D. B., XING, C., (2009). Nonparametric Bayes modeling of multivariate categorical data, Journal of the American Statistical Association, 104, pp. 1042-1051.
  20. DENG, Y., CHANG, C., IDO, M.S., LONG, Q., (2016). Multiple imputation for general missing data patterns in the presence of high-dimensional data. Scientific Reports, 6.
  21. DOOVE, LISA, L., VAN BUUREN, S., ELISE, D., (2014). Recursive Partitioning for Missing Data Imputation in the Presence of Interaction Effects, Computational Statistics and Data Analysis, Elsevier, 72, pp. 92-104.
  22. EROSHEVA E. A., FIENBERG S. E., JUNKER B. W. (2002). Alternative statistical models and representations for large sparse multi-dimensional contingency tables, Annales de la Faculté des Sciences de Toulouse, 11, pp. 485-505.
  23. FICHMAN, M., CUMMINGS, J. N., (2003). Multiple Imputation for Missing Data: Making the most of What you Know, Organizational Research Methods, 6(3), pp. 282-308.
  24. FINCH, W. H., (2010). Imputation methods for missing categorical questionnaire data: A comparison of approaches. Journal of Data Science, 8, pp. 361-378.
  25. GELMAN, A., SPEED, T. P., (1993). Characterizing a joint probability distribution by conditionals, Journal of the Royal Statistical Society Series B: Statistical Methodology, 55, pp. 185-188.
  26. GRAHAM, J. W., SCHAFER, J. L., (1999). On the performance of multiple imputation for multivariate data with small sample size. In R. H. Hoyle (Ed.), Statistical strategies for small sample research, Thousand Oaks, CA: Sage, pp.1-29.
  27. GENEVIEVE, R., OLGA, K., JULIE, J., ÉRIC M., ROBERT, T., (2018). Main effects and interactions in mixed and incomplete data frames. arXiv preprint, arXiv:1806.09734.
  28. HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J., (2001). The Elements of Statistical Learning; Data Mining, Inference, and Prediction, second ed. Springer Verlag, New York.
  29. HIRANO, K., (2002). Semiparametric Bayesian inference in autoregressive panel data models. Econometrica, 70, pp. 781-799.
  30. HAREL, O., SCHAFER, J. L., (2003). Multiple Imputation in two Stages. Proceedings of the Federal Committee on Statistical Methodology Research Conference, Washington D. C.
  31. HORTON, N. J., LIPSITZ, S. P., PARZEN, M., (2003). A potential for bias when rounding in multiple imputation. The American Statistician, 57, pp. 229-232.
  32. HAREL, O., (2007). Inferences on missing information under multiple imputation and two-stage multiple imputation. Statistical Methodology, 4, pp. 75-89.
  33. HE, Y., (2010). Missing data analysis using multiple imputation: getting to the heart of the matter. Circ Cardiovasc Qual Outcomes, 3, pp. 98-105.
  34. HASTIE, T., MAZUMDER, R., LEE, J. D., ZADEH,R., (2015). Matrix completion and low-rank svd via fast alternating least squares, J. Mach. Learn. Res., 16(1), pp. 3367-3402.
  35. HOLDER, L., (2015). Multiple Imputation in Complex Survey Settings: A Comparison of Methods within the Health Behaviour in School-aged Children Study, Queen's University
  36. HUSSON, F., J. JOSSE, B. NARASIMHAN, G. ROBIN., (2018). Imputation of mixed data with multilevel singular value decomposition, arXiv e-prints, arXiv:1804.11087.
  37. IACUS, S. M., PORRO, G., (2007). Missing data imputation, matching and other applications of random recursive partitioning. Comput. Statist. Data Anal, 52, pp. 773-789.
  38. IACUS, S. M., PORRO, G., (2008). Invariant and metric free proximities for data matching: an R package. J. Stat. Softw, 25, pp. 1-22.
  39. KIM, H., LOH, W.Y., (2001). Classification trees with unbiased multiway splits. Journal of the American Statistical Association, 96, pp. 589-604.
  40. KYUNG, M., GILL, J., CASELLA, G., (2010). Estimation in Dirichlet random effects models. Annals of Statistics, 38, pp.979-1009.
  41. WIRTH, K. E., TCHETGEN TCHETGEN, E. J., (2014). Accounting for selection bias in association studies with complex survey data. Epidemiology (Cambridge, Mass.), 25(3), pp. 444-453.
  42. LOH, W., SHIH, Y., (1997). Split selection methods for classification trees. Statistica Sinica, 7, pp. 815-840.
  43. LITTLE, R. J. A., RUBIN, D. B., (2002). Statistical analysis with missing data (2nd ed.). New York: Wiley.
  44. LEE, K.J., GALATI, J. C., SIMPSON, J. A., CARLIN, J. B., (2012). Comparison of methods for imputing ordinal data using multivariate normal imputation: a case study of non-linear effects in a large cohort study. Stat Med, 31(30), pp. 4164-74.
  45. LI, D., GU, H., ZHANG, L.Y., (2013). A hybrid genetic algorithm-fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals. J. Soft Computing, 17, pp. 1787-1796.
  46. LIANG, Z., ZHIKUI, C., ZHENNAN, Y., YUEMING, HU., (2015). A Hybrid Method for Incomplete Data Imputation. 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, New York, pp. 1725-1730.
  47. LIYONG, Z., WEI, L., XIAODONG, L., WITOLD, P., CHONGQUAN, Z., LU, W., (2016). A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value, International Journal of Intelligent Systems, 31(4), pp. 297-313.
  48. LOH, W. Y., ELTINGE, J., CHO, M., LI, Y., (2016). Classification and Regression Tree Methods for Incomplete Data from Sample Surveys, arXiv preprint arXiv:1603.01631.
  49. 56 H. Razzak , Ch. Heumann: Hybrid multiple imputation...
  50. LEE, K. J., CARLIN, J. B., (2017). Multiple imputation in the presence of non-normal data. Stat Med, 36(4), pp. 606-17.
  51. MARKER, D. A., JUDKINS, D. R., WINGLEE, M. (2002), Large-Scale Imputation for Complex Surveys. Survey Nonresponse, Wiley: New York, pp. 329-341.
  52. MOONS, K. G. M., DONDERS, R. A. R. T., STIJNEN, T., HARRELL, F. E., (2006). Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol, 59(10), pp. 1092-101.
  53. MORRIS, T. P., IAN, R. W., PATRICK, R., (2014). Tuning Multiple Imputation by Predictive Mean Matching and Local Residual Draws. BMC Medical Research Methodology, BioMed Central, 14(1), 75.
  54. MARSHALL, R. J., KITSANTAS, P., (2012). Stability and structure of cart and span search generated data partitions for the analysis of low birth weight. J. Data Sci, 10, pp. 61-73.
  55. MURRAY, J. S., REITER, J. P., (2016). Multiple imputation of missing categorical and continuous values via Bayesian mixture models with local dependence. Journal of the American Statistical Association, 111, pp. 1466-1479.
  56. NONYANE, B. A. S., FOULKES, A. S., (2007). Multiple imputation and random forests (MIRF) for unobservable, high-dimensional data. Int J Biostat, 3, pp. 1-18.
  57. NISHANTH, K. J., RAVI, V., ANKAIAH, N., BOSE, I., (2012). Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts. Expert Sys Appl, 39(12), pp. 10583-10589.
  58. NISHANTH, K. J., RAVI, V., (2013). A computational intelligence based online data imputation method: An application for banking. J. Inf. Process. Syst. 9, pp. 633-650.
  59. NIKFALAZAR, S., YEH C. H., BEDINGFIELD, S., KHORSHIDI, H. A., (2019). A Hybrid Missing Data Imputation Method for Constructing City Mobility Indices. In: Islam R. et al. (eds.) Data Mining. AusDM 2018. Communications in Computer and Information Science, Vol. 996. Springer, Singapore.
  60. OBA, S., SATO, M., TAKEMASA, I., MONDEN, M., MATSUBARA, K., ISHII, S., (2003). A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19, pp. 2088-2096.
  61. QUANLI, W., DANIEL, M.V., REITER, J. P., JIGCHEN, H., (2018). NPBayesImputeCat: Non-Parametric Bayesian Multiple Imputation for Categorical Data. R package version 0.1, https://CRAN.R-project.org/package=NPBayesImputeCat.
  62. RUBIN, D. B., (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley.
  63. RAGHUNATHAN, T. W., LEPKOWKSI, J. M., VAN HOEWYK, J., SOLENBEGER, P. A., (2001). Multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27, pp. 85-95.
  64. RUBIN, D. B., (2003). Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57(1), pp. 3-18.
  65. REITER, J. P., DRECHSLER, J., (2007). Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality. IAB Discussion Paper, 20, pp. 1-18.
  66. REITER, J. P., RAGHUNATHAN, T. E., (2007). The multiple adaptions of multiple imputation, Journal of the American Statistical Association, 102, pp. 1462-1471.
  67. RODRI´GUEZ, A., DUNSON, D. B., (2011). Nonparametric Bayesian models through probit stick-breaking processes. Bayesian Analysis, 6, pp. 145-178.
  68. R Core Team (2018). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.Rproject.org/.
  69. SCHAFER, J. L., (1997). Analysis of Incomplete Multivariate Data. London: Chapman and Hall.
  70. STROBL, C., MALLEY, J., ZEILEIS, A., (2009). An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychol. Methods, 14, pp. 323-348.
  71. SU, Y.S., GELMAN, A., HILL, J., YAJIMA, M., (2011). Multiplebimputation with diagnostics (mi) in R: Opening windows into the black box. Journal of Statistical Software, 45(2), pp. 1-31.
  72. SEAMAN, S., BARTLETT, J., WHITE, I., (2012). Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. BMC Med Res Methodol, 12(1), pp. 1-13.
  73. STEKHOVEN, D. J., BÜHLMANN, P., (2012). MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics, 28, pp.112-118.
  74. SI, Y., REITER, J. P., (2013). Nonparametric Bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys. Journal of Educational and Behavioral Statistics, 38, pp. 499-521.
  75. SHAH, A.D., JONATHAN, W. B., JAMES, C., OWEN, N., HARRY, H., (2014). Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using Mice: A Caliber Study. American Journal of Epidemiology, 179 (6). Oxford University Press, pp. 764-74.
  76. SHUKUR, O. B., LEE, M. H., (2015). Imputation of missing values in daily wind speed data using hybrid AR-ANN method. Modern Applied Science.
  77. TEMPL, M., ANDREAS, A., ALEXANDER, K., BERND, P., (2012). VIM: Visualization and Imputation of Missing Values, http://cran.r-project.org/web/packages/VIM/VIM.pdf.
  78. TING, J., YU, B., YU, D., MA, S., (2014). Missing data analyses: a hybrid multiple imputation algorithm using gray system theory and entropy based on clustering, Applied intelligence, 40(2), pp. 376-388.
  79. TANG, J., ZHANG, G., WANG, Y., WANG, H., LIU, F., (2015). A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transportation Research Part C: Emerging Technologies, 51, pp. 29-40.
  80. THOMAS, L., (2019). mitools: Tools for Multiple Imputation of Missing Data. R package version 2.4, https://CRAN.R-project.org/package=mitools.
  81. VAN BUUREN, S., OUDSHOORN, C. G. M., (1999). Flexible multivariate imputation by MICE. Tech. rep., TNO Prevention and Health, Leiden.
  82. VAN BUUREN, S., GROOTHUIS-OUDSHOON, K., (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), pp. 1-67.
  83. VAN BUUREN, S., (2007). Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification. Statistical Methods in Medical Research, Sage Publications Sage UK: London, England, 16(3), pp. 219-42.
  84. VERMUNT, J. K., VAN GINKEL, J. R., VAN DER ARK, L. A., SIJTSMA, K., (2008). Multiple imputation of incomplete categorical data using latent class analysis. Sociological Methodology, 38, pp. 369-397.
  85. VAN BUUREN, S., (2012). Flexible imputation of missing data. Boca Raton: CRC Press.
  86. WHITE, I. R., ROYSTON, P., WOOD, A. M., (2011). Multiple imputation using chained equations: issues and guidance for practice. Stat Med, 30(4), pp. 377-99.
  87. WHITE, I.R., CARLIN, J. B., (2010). Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Stat Med, 29(28), pp. 2920-31.
  88. WEIRICH, S., HAAG, N., HECHT, M., BÖHME, K., SIEGLE, T., LÜDTKE, O., (2014). Nested multiple imputation in large-scale assessments. Large Scale Assess. Educ., 2, pp. 1-18.
  89. XIE, X., MENG, X.-L., (2017). Dissecting multiple imputation from a multi-phase inference perspective: what happens when God's, imputer's and analyst's models are uncongenial? Statistica Sinica 27, pp. 1485-1594 (including discussion).
  90. YUCEL, R.M., HE, Y., ZASLAVSKY, A. M., (2011). Gaussian-based routines to impute categorical variables in health surveys. Stat Med, 30(29), pp. 3447-60.
  91. ZHU, J., M., EISELE, M., (2013). Multiple Imputation in a Complex Household Survey, The German Panel on Household Finances (PHF): Challenges and Solutions. PHF User Guide.
  92. ZHAO, Y., LONG, Q., (2016). Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research, 25, pp. 2021-2035.
Cited by
Show
ISSN
1234-7655
Language
eng
URI / DOI
http://dx.doi.org/10.21307/stattrans-2019-033
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu