Recursive partitioning on incomplete data using surrogate decisions and multiple imputation

Computational Statistics and Data Analysis

Volumn 56, Issue 6, 2012, Pages 1552-1565

  Hapfelmeier, A ^a   Hothorn, T ^b   Ulm, K ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Classification and regression trees; MICE; Multiple imputation; Random Forests; Recursive partitioning; Surrogates

Indexed keywords

CLASSIFICATION AND REGRESSION TREE; MICE; MULTIPLE IMPUTATION; RANDOM FORESTS; RECURSIVE PARTITIONING; SURROGATES;

DATA STRUCTURES; DECISION TREES; FORESTRY; MAMMALS;

REGRESSION ANALYSIS;

DATA; FORESTRY; MAMMALS; REGRESSION ANALYSIS;

EID: 84857652587     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2011.09.024     Document Type: Article

References (49)

1. T. Allison, and D.V. Cicchetti Sleep in mammals: ecological and constitutional correlates Science 194 1976 732 734 http://www.sciencemag.org/ cgi/reprint/194/4266/732.pdf
2. Asuncion, A.; Newman, D.J.; 2007. UCI machine learning repository.
3. A.L. Boulesteix, C. Strobl, T. Augustin, and M. Daumer Evaluating microarray-based classifiers: an overview Cancer Information 6 2008 77 97
4. L. Breiman Bagging predictors Machine Learning 24 1996 123 140
5. L. Breiman Random forests Machine Learning 45 2001 5 32
6. L. Breiman, J. Friedman, C.J. Stone, and R.A. Olshen Classification and Regression Trees 1984 Chapman & Hall, CRC
7. P. Bhlmann, and B. Yu Analyzing bagging Annals of Statistics 30 2002 927 961
8. L.F. Burgette, and J.P. Reiter Multiple imputation for missing data via sequential regression trees American Journal of Epidemiology 172 2010 1070 1076 http://aje.oxfordjournals.org/content/172/9/1070.full.pdf+html
9. J.M. Chambers Graphical Methods for Data Analysis (Statistics) 1983 Chapman & Hall, CRC
10. M. Elter, R. Schulz-Wendtland, and T. Wittenberg The prediction of breast cancer biopsy outcomes using two cad approaches that both emphasize an intelligible decision process Medical Physics 34 2007 4164 4172
11. A. Farhangfar, L. Kurgan, and J. Dy Impact of imputation of missing values on classification error for discrete data Pattern Recognition 41 2008 3692 3705
12. A.J. Feelders Handling missing data in trees: surrogate splits or statistical imputation PKDD'99: Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery 1999 Springer-Verlag London, UK 329 334
13. Haberman, S.J.; 1976. Generalized residuals for log-linear models. In: Proceedings of the 9th International Biometrics Conference, pp. 104122.
14. O. Harel, and X.H. Zhou Multiple imputation: review of theory, implementation and software Statistics in Medicine 26 2007 3057 3077
15. Y. He, A. Zaslavsky, M. Landrum, D. Harrington, and P. Catalano Multiple imputation in a large-scale complex survey: a practical guide Statistical Methods in Medical Research 2009
16. S.G. Hilsenbeck, and G.M. Clark Practical p-value adjustment for optimally selected cutpoints Statistics in Medicine 15 1996 103 112
17. N.J. Horton, and K.P. Kleinman Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models The American Statistician 61 2007 79 90
18. Hothorn, T.; Hornik, K.; Strobl, C.; Zeileis, A.; 2008. Party: a laboratory for recursive part(y)itioning. R Package Version 0.99993.
19. T. Hothorn, K. Hornik, and A. Zeileis Unbiased recursive partitioning Journal of Computational and Graphical Statistics 15 2006 651 674 http://pubs.amstat.org/doi/pdf/10.1198/106186006X133933
20. K.J. Janssen, A.R. Donders, F.E. Harrell, Y. Vergouwe, Q. Chen, D.E. Grobbee, and K.G. Moons Missing covariate data in medical research: to impute is better than to ignore Journal of Clinical Epidemiology 63 2010 721 727
21. K.J. Janssen, Y. Vergouwe, A.R. Donders, F.E. Harrell, Q. Chen, D.E. Grobbee, and K.G. Moons Dealing with missing predictor values when applying clinical prediction models Clinical Chemistry 55 2009 994 1001
22. M.A. Klebanoff, and S.R. Cole Use of multiple imputation in the epidemiologic literature American Journal of Epidemiology 168 2008 355 357 http://aje.oxfordjournals.org/content/168/4/355.full.pdf+html
23. B. Lausen, W. Sauerbrei, and M. Schumacher Classification and regression trees (cart) used for the exploration of prognostic factors measured on different scales P. Dirschedl, R. Ostermann, Computational Statistics 1994 Physica-Verlag Heidelberg 483 496
24. A. Liaw, and M. Wiener Classification and regression by randomforest R News 2 2002 18 22
25. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data, Second Edition 2nd ed. 2002 Wiley-Interscience
26. K. Lunetta, B.L. Hayward, J. Segal, and P. VañEerdewegh Screening large-scale association study data: exploiting interactions using random forests BMC Genetics 5 2004
27. P. Messeri, G. Lee, D.M. Abramson, A. Aidala, M.A. Chiasson, and D.J. Jessop Antiretroviral therapy and declining aids mortality in New York city Journal of Medical Care 4 2003 512 521
28. F. Mosteller, and J.W. Tukey Data Analysis and Regression: A Second Course in Statistics 1977 Addison-Wesley Pub. Co.
29. K. Nicodemus, J. Malley, C. Strobl, and A. Ziegler The behaviour of random forest permutation-based variable importance measures under predictor correlation BMC Bioinformatics 11 2010 110 +
30. R.K. Pearson The problem of disguised missing data SIGKDD Explorations Newsletter 8 2006 83 92
31. J.R. Quinlan C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning) 1st ed. 1993 Morgan Kaufmann
32. R Development Core Team, 2010. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN: 3-900051-07-0.
33. Rieger, A.; Hothorn, T.; Strobl, C.; 2010. Random forests with missing values in the covariates.
34. D.B. Rubin Inference and missing data Biometrika 63 1976 581 592 http://biomet.oxfordjournals.org/cgi/reprint/63/3/581.pdf
35. D.B. Rubin Multiple Imputation for Nonresponse in Surveys 1987 J. Wiley & Sons New York
36. D.B. Rubin Multiple imputation after 18 + years Journal of the American Statistical Association 91 1996 473 489
37. J.L. Schafer Analysis of Incomplete Multivariate Data 1997 Chapman & Hall
38. J.L. Schafer, and J.W. Graham Missing data: our view of the state of the art Psychological Methods 7 2002 147 177
39. H. Strasser, and C. Weber On the asymptotic theory of permutation statistics Mathematical Methods of Statistics 2 1999
40. C. Strobl, A.L. Boulesteix, and T. Augustin Unbiased split selection for classification trees based on the gini index Computational Statistics & Data Analysis 52 2007 483 501
41. C. Strobl, A.L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis Conditional variable importance for random forests BMC Bioinformatics 9 2008 307 +
42. C. Strobl, A.L. Boulesteix, A. Zeileis, and T. Hothorn Bias in random forest variable importance measures: illustrations, sources and a solution BMC Bioinformatics 8 2007 25 +
43. C. Strobl, J. Malley, and G. Tutz An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests Psychological Methods 14 2009 323 348
44. M. Templ, A. Kowarik, and P. Filzmoser Iterative stepwise regression imputation using standard and robust methods Computational Statistics & Data Analysis 55 2011 2793 2806
45. Therneau, T.M.; Atkinson, B.; 2009. rpart: recursive partitioning. R Package Version 3.145; R Port by B. Ripley.
46. S. van Buuren Multiple imputation of discrete and continuous data by fully conditional specification Statistical Methods in Medical Research 16 2007 219 242 http://smm.sagepub.com/cgi/reprint/16/3/219.pdf
47. S. Van Buuren, J.P.L. Brand, C.G.M. Groothuis-Oudshoorn, and D.B. Rubin Fully conditional specification in multivariate imputation Journal of Statistical Computation and Simulation 76 2006 1049 1064
48. van Buuren, S.; Groothuis-Oudshoorn, K.; 2011. Mice: multivariate imputation by chained equations in r. Journal of Statistical Software, pp. 168 (in press).
49. W.N. Venables, and B.D. Ripley Modern Applied Statistics with S 4th ed. 2003 Springer New York, USA