Recursive partitioning for missing data imputation in the presence of interaction effects

Computational Statistics and Data Analysis

Volumn 72, Issue , 2014, Pages 92-104

  Doove, L L ^a,b   Van Buuren, S ^a,c   Dusseldorp, E ^b,c  

^a UTRECHT UNIVERSITY   (Netherlands)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c TNO   (Netherlands)

Author keywords

CART; Classification and regression trees; Interaction problem; MICE; Nonlinear relations; Random forests

Indexed keywords

CART; CLASSIFICATION AND REGRESSION TREE; INTERACTION PROBLEMS; MICE; NONLINEAR RELATIONS; RANDOM FORESTS;

DECISION TREES; PARAMETER ESTIMATION;

REGRESSION ANALYSIS;

EID: 84888869357     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2013.10.025     Document Type: Article

References (34)

1. L.S. Aiken, and S.G. West Multiple Regression: Testing and Interpreting Interactions 1991 Sage Newbury Park, CA
2. L. Breiman Random forest Mach. Learn. 45 2001 5 32
3. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and Regression Trees 1984 Wadsworth & Brooks/Cole Advanced Books & Software Monterey, CA
4. L.F. Burgette, and K.P. Reiter Multiple imputation for missing data via sequential regression trees Amer. J. Epidemiol. 172 2010 1070 1076
5. J. Cohen Statistical Power Analysis for the Behavioral Sciences 1988 Lawrence Erlbaum Associates Hillsdale, New Jersey
6. L.M. Collins, J.L. Schafer, and C.M. Kam A comparison of inclusive and restrictive strategies in modern missing data procedures Psychol. Methods 6 2001 330 351 (Pubitemid 33599063)
7. E. Dusseldorp, C. Conversano, and B.J. Van Os Combining an additive and tree-based regression model simultaneously: STIMA J. Comput. Graph. Statist. 19 2010 514 530
8. J.H. Friedman Multivariate adaptive regression splines Ann. Statist. 19 1991 1 67
9. J.W. Graham, A.E. Olchowski, and T.D. Gilreath How many imputations are really needed? Some practical clarifications of multiple imputation theory Prevention Sci. 8 2007 206 213
10. C. Haddock, D. Rindskopf, and W. Shadish Using odds ratios as effect sizes for meta-analysis of dichotomous data: a primeur on methods and issues Psychol. Methods 3 1998 339 353
11. D.J. Hand Construction and Assessment of Classification Rules 1997 Wiley Chichester
12. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning; Data Mining, Inference, and Prediction second ed. 2001 Springer Verlag New York
13. T. Hothorn, K. Hornik, and A. Zeileis Unbiased recursive partitioning: a conditional inference framework J. Comput. Graph. Statist. 15 2006 651 674 (Pubitemid 44554201)
14. Hothorn, T., Zeileis, A., 2013. Partykit: a toolkit for recursive partitioning. R package version 0.1-6. URL: http://CRAN.R-project.org/package= partykit.
15. S.M. Iacus, and G. Porro Missing data imputation, matching and other applications of random recursive partitioning Comput. Statist. Data Anal. 52 2007 773 789 (Pubitemid 47552603)
16. S.M. Iacus, and G. Porro Invariant and metric free proximities for data matching: an R package J. Stat. Softw. 25 2008 1 22
17. G.V. Kass An exploratory technique for investigating large quantities of categorical data J. R. Stat. Soc. 29 1980 119 127
18. A. Liaw, and M. Wiener Classification and regression by randomforest R News 2 2002 18 22 URL: http://CRAN.R-project.org/package=randomForest
19. W.Y. Loh Regression trees with unbiased variable selection and interaction detection Statist. Sinica 12 2002 361 386
20. A. Lubin The interpretation of significant interaction Educ. Psychol. Meas. 21 1961 807 817
21. R.J. Marshall, and P. Kitsantas Stability and structure of cart and span search generated data partitions for the analysis of low birth weight J. Data Sci. 10 2012 61 73
22. E.C. Merkle, and V.A. Schaffer Binary recursive partitioning: background, methods, and application to psychology British J. Math. Statist. Psych. 64 2011 161 181
23. J.N. Morgan, and J.A. Sonquist Problems in the analysis of survey data, and a proposal J. Amer. Statist. Assoc. 58 1963 415 434
24. B.A.S. Nonyane, and A.S. Foulkes Multiple imputation and random forests (mirf) for unobservable, high dimensional data Int. J. Biostat. 3 2007 Article 12
25. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers
26. R Development Core Team. R: A Language and Environment for Statistical Computing 2013 R Foundation for Statistical Computing Vienna, Austria URL: http://www.R-project.org/
27. D.B. Rubin Multiple imputation after 18+~years J. Am. Stat. Assoc. 91 1996 473 489
28. J. Schepers, and I. Van Mechelen A two-mode clustering method to capture the nature of the dominant interaction pattern in large profile data matrices Psychol. Methods 16 2011 361 371
29. D.J. Stekhoven, and P. Bühlmann Missforest - non-parametric missing value imputation for mixed-type data Bioinformatics 1 2012 112 118
30. C. Strobl, J. Malley, and A. Zeileis An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests Psychol. Methods 14 2009 323 348
31. Therneau, T., Atkinson, B., Ripley, B., 2013. rpart: recursive partitioning. R package version 4.1-3. URL: http://CRAN.R-project.org/package= rpart.
32. S. Van Buuren Multiple imputation of discrete and continuous data by fully conditional specification Stat. Methods Med. Res. 16 2007 219 242
33. S. Van Buuren Flexible Imputation of Missing Data 2012 Chapman & Hall/CRC Boca Raton, FL
34. S. Van Buuren, and K. Groothuis-Oudshoorn MICE: multivariate imputation by chained equations in R J. Stat. Softw. 45 2011 1 67 URL: http://CRAN.R- project.org/package=mice