RE-EM trees: A data mining approach for longitudinal and clustered data

Machine Learning

Volumn 86, Issue 2, 2012, Pages 169-207

  Sela, Rebecca J ^a   Simonoff, Jeffrey S ^b  

^a JP Morgan Chase and Co   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

CART; Clustered data; Longitudinal data; Mixed effects model; Panel data; Random effects; Regression tree

Indexed keywords

CART; CLUSTERED DATA; LONGITUDINAL DATA; MIXED EFFECTS MODELS; PANEL DATA; RANDOM EFFECTS; REGRESSION TREES;

ESTIMATION; FORESTRY; GRAPHICAL USER INTERFACES; MATHEMATICAL MODELS; POPULATION STATISTICS; RANDOM PROCESSES; REGRESSION ANALYSIS;

TREES (MATHEMATICS);

CORRELATION; DATA PROCESSING; GRAPHICAL USER INTERFACES; MATHEMATICAL MODELS; RANDOM PROCESSES; REGRESSION ANALYSIS; STATISTICS;

EID: 84868300223     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5258-3     Document Type: Article

References (39)

1. Abdolell, M., LeBlanc, M., Stephens, D., & Harrison, R. V. (2002). Binary partitioning for continuous longitudinal data: categorizing a prognostic variable. Statistics in Medicine, 21, 3395-3409. (Pubitemid 35314658)
2. Afshartous, D., & de Leeuw, J. (2005). Prediction in multilevel models. Journal of Educational and Behavioral Statistics, 30, 109-139. (Pubitemid 40882647)
3. Becker, R. A., Cleveland,W. S., & Shyu, M.-J. (1996). The visual design and control of trellis display. Journal of Computational and Graphical Statistics, 5, 123-155.
4. Berk, R. A. (2008). Statistical learning from a regression perspective. New York: Springer.
5. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey: Wadsworth.
6. De'Ath, G. (2002). Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology, 83, 1105-1117.
7. De'Ath, G. (2006). mvpart: multivariate partitioning. R package version 1.2-4.
8. Dee, T. S., & Sela, R. J. (2003). The fatality effects of highway speed limits by gender and age. Economics Letters, 79, 401-408. (Pubitemid 36507466)
9. Evgeniou, T., Pontil, M., & Toubia, O. (2007). A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Science, 26, 805-818.
10. Galimberti, G., & Montanari, A. (2002). Regression trees for longitudinal data with time-dependent covariates. In K. Jajuga, A. Sokolowski, & H.-H. Bock (Eds.), Classification, clustering and data analysis (pp. 391-398). New York: Springer.
11. Ghose, A., Ipeirotis, P., & Sundararajan, A. (2005). The dimensions of reputation in electronic markets (Technical Report 06-02). NYU CeDER Working Paper.
12. Hajjem, A., Bellavance, F., & Larocque, D. (2008). Mixed-effects regression trees for clustered data. Les Cahiers du GERAD G-2008-57.
13. Hajjem, A., Bellavance, F., & Larocque, D. (2011).Mixed effects regression trees for clustered data. Statistics and Probability Letters, 81, 451-459.
14. Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72, 320-340.
15. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
16. Hsiao, W.-C., & Shih, Y.-S. (2007). Splitting variable selection for multivariate regression trees. Statistics and Probability Letters, 77, 265-271. (Pubitemid 44835361)
17. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38, 963-974. (Pubitemid 14272804)
18. Larsen, D. R., & Speckman, P. L. (2004). Multivariate regression trees for analysis of abundance data. Biometrics, 60, 543-549. (Pubitemid 38780217)
19. Lee, S. K. (2005). On generalized multivariate decision tree by using GEE. Computational Statistics & Data Analysis, 49, 1105-1119. (Pubitemid 40710162)
20. Lee, S. K. (2006). On classification and regression trees for multiple responses and its application. Journal of Classification, 23, 123-141. (Pubitemid 44099762)
21. Lee, S. K., Kang, H.-C., Han, S.-T., & Kim, K.-H. (2005). Using generalized estimating equations to learn decision trees with multivariate responses. Data Mining and Knowledge Discovery, 11, 273-293. (Pubitemid 41669195)
22. Liu, Z., & Bozdogan, H. (2004). Improving the performance of radial basis function (RBF) classification using information criteria. In H. Bozdogan (Ed.), Statistical data mining and knowledge discovery (pp. 193-216). Boca Raton: Chapman and Hall/CRC.
23. Liu, C., & Rubin, D. B. (1994). The ECME algorithm: a simple extension of EM and ECM with faster monotone convergence. Biometrika, 81, 633-648.
24. Loh, W.-Y. (2002). Regression trees with unbiased variable selection and interaction detection. Statistica Sinica, 12, 361-386. (Pubitemid 36116175)
25. Milborrow, S. (2011). rpart.plot: plot rpart models. R package version 1.2-2.
26. Patterson, H. D., & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58, 545-554.
27. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & the R Core team (2009). nlme: linear and nonlinear mixed effects models. R package version 3.1-93.
28. R Development Core Team (2009). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0. URL http://www.R-project.org.
29. Ritschard, G., & Oris, M. (2005). Life course data in demography and social sciences: statistical and data mining approaches. In R. Levy, P. Ghisletta, J.-M. Le Goff, D. Spini, & E. Widmer (Eds.), Towards an interdisciplinary perspective on the life course, advances in life course research (pp. 289-320). Amsterdam: Elsevier.
30. Ritschard, G., Gabadinho, A., Müller, N. S., & Studer, M. (2008). Mining event histories: a social science perspective. International Journal of Data Mining, Modelling and Management, 1, 68-90.
31. Segal, M. R. (1992). Tree-structured models for longitudinal data. Journal of the American Statistical Association, 87, 407-418.
32. Sela, R. J., & Simonoff, J. S. (2009). RE-EM trees: a new data mining approach for longitudinal data. NYU Stern Working Paper SOR-2009-03.
33. Simonoff, J. S. (2003). Analyzing categorical data. New York: Springer.
34. Therneau, T. M., & Atkinson, B. (2010). rpart: recursive partitioning. R port by Brian Ripley. R package version 3.1-46.
35. Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan Kauffman.
36. Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer.
37. West, B. T., Welch, K. B., & Galecki, A. T. (2007). Linear mixed models: a practical guide using statistical software. Boca Raton: Chapman and Hall/CRC.
38. Zhang, H. (1997). Multivariate adaptive splines for analysis of longitudinal data. Journal of Computational and Graphical Statistics, 6, 74-91.
39. Zhang, H. (1998). Classification trees for multiple binary responses. Journal of the American Statistical Association, 93, 180-193.