Predictive mean matching imputation of semicontinuous variables

Statistica Neerlandica

Volumn 68, Issue 1, 2014, Pages 61-90

  Vink, Gerko ^a,b   Frank, Laurence E ^a   Pannekoek, Jeroen ^b   van Buuren, Stef ^a,c  

^a UTRECHT UNIVERSITY   (Netherlands)

^b Division of Methodology and Quality   (Netherlands)

^c TNO   (Netherlands)

Author keywords

Multiple imputation; Point mass; Predictive mean matching; Semicontinuous data; Skewed data

Indexed keywords

EID: 84893057912     PISSN: 00390402     EISSN: 14679574     Source Type: Journal    
DOI: 10.1111/stan.12023     Document Type: Article

References (28)

1. Abayomi, K., A. Gelman and M. Levy (2008), Diagnostics for multivariate imputations, Journal of the Royal Statistical Society: Series C: Applied Statistics 57, 273-291.
2. Alfons, A., M. Templ and P. Filzmoser (2010a), Applications of statistical simulation in the case of eu-silc: using the r package simframe, Journal of Statistical Software 37, 17.
3. Alfons, A., M. Templ and P. Filzmoser (2010b), An object-oriented framework for statistical simulation: the r package simframe, Journal of Statistical Software 37, 1-36.
4. Amemiya, T. (1984), Tobit models: a survey, Journal of Econometrics 24, 3-61.
5. Chambers, R. and R. Clark (2012), An introduction to model-based survey sampling with applications, Oxford University Press.
6. Duan, N., W. G., Manning JR., C. N., Morris and J. P. Newhouse (1983), A comparison of alternative models for the demand for medical care, Journal of Business & Economic Statistics 1, 115-126.
7. Heckman, J. (1974), Shadow prices, market wages, and labor supply, Econometrica 42, 679-694.
8. Heckman, J. (1976), The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. NBER Chapters, pages 120-137.
9. Heeringa, S., R. Little and T. Raghunathan (2002), Survey nonresponse, chapter Multivariate imputation of coarsened survey data on household wealth, pages 357-371. Wiley.
10. Javaras, K. N. and D. A. Van Dyk (2003), Multiple imputation for incomplete data with semicontinuous variables, Journal of the American Statistical Association 98, 703-715.
11. Little, R. (1988), Missing-data adjustments in large surveys, Journal of Business and Economic Statistics 6, 287-296.
12. Little, R. J. A. and D. B. Rubin (2002), Statistical analysis with missing data. Wiley-Interscience, New York.
13. Manning, W., C. Morris, J. Newhouse, L. Orr, N. Duan, E. Keeler, A. Leibowitz, K. Marquis, M. Marquis and C. Phelps (1981), A two-part model of the demand for medical care: preliminary results from the health insurance study. Economics and Health Economics. Amsterdam: North-Holland.
14. Olkin, I. and R. Tate (1961), Multivariate correlation models with mixed discrete and continuous variables. The Annals of Mathematical Statistics 32, 448-465.
15. Olsen, M. K. and J. L. Schafer (2001), A two-part random-effects model for semicontinuous longitudinal data. Journal of the American Statistical Association 96, 730-745.
16. Raghunathan, T., P. Solenberger and J. Van Hoewyk (2002), IVEware: imputation and variance estimation software. Ann Arbor, MI: Survey Methodology Program, Survey Research Center, Institute for Social Research, University of Michigan.
17. Royston, P. (2005), Multiple imputation of missing values: update of ice. Stata Journal 5, 527-536.
18. Rubin, D. (1987), Multiple imputation for nonresponse in surveys. John Wiley and Sons, New York.
19. Sargasso.nl (2012), De haagse twitter stolp.
20. Schafer, J. L. (1997), Analysis of incomplete multivariate data. Chapman & Hall/CRC, London.
21. Schafer, J. and M. Olsen (1999), Modeling and imputation of semicontinuous survey variables. In Proceedings of the Federal Committee on Statistical Methodology Research Conference.
22. Su, Y., A. Gelman, J. Hill and M. Yajima (2011), Multiple imputation with diagnostics (mi) in r: opening windows into the black box, Journal of Statistical Software 45, 1-31.
23. Templ, M., A. Kowarik and P. Filzmoser (2011), Iterative stepwise regression imputation using standard and robust methods. Computational Statistics and Data Analysis 55, 2793-2806.
24. Tobin, J. (1958), Estimation of relationships for limited dependent variables, Econometrica 26, 24-36.
25. Van Buuren, S. (2012), Flexible imputation of missing data. Chapman & Hall/CRC, Boca Raton, FL.
26. Van Buuren, S. and C. Groothuis-Oudshoorn (2011), MICE: multivariate imputation by chained equations in R. Journal of Statistical Software 45, 1-67.
27. White, I., P. Royston and A. Wood (2011), Multiple imputation using chained equations: issues and guidance for practice, Statistics in Medicine 30, 377-399.
28. Yu, L., A. Burton and O. Rivero-Arias (2007), Evaluation of software for multiple imputation of semicontinuous data. Statistical Methods in Medical Research, 16, 243-258.