Using generalized estimating equations to learn decision trees with multivariate responses

Data Mining and Knowledge Discovery

Volumn 11, Issue 3, 2005, Pages 273-293

  Lee, Seong Keon ^a   Kang, Hyun Cheol ^b   Han, Sang Tae ^b   Kim, Kwang Hwan ^c  

^a Korea University   (South Korea)

^b Hoseo University   (South Korea)

^c Dankook University   (South Korea)

Author keywords

Generalized estimating equations (GEE); Multiple responses; Multivariate decision tree

Indexed keywords

DECISION THEORY; LEARNING SYSTEMS; STATISTICAL METHODS; TREES (MATHEMATICS); WORLD WIDE WEB;

GENERALIZED ESTIMATING EQUATIONS (GEE); MAHALANOBIS DISTANCE; MULTIPLE RESPONSES; MULTIVARIATE DECISION TREES;

ALGORITHMS;

EID: 27944460344     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-005-0004-8     Document Type: Review

References (26)

1. Agresti, A. 1990. Categorical Data Analysis. NY: Wiley.
2. Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. 1984. Classification and Regression Trees. Wadsworth, Belmont.
3. Cappelli, C., Molla, F., and Siciliano, R. 1998. An alternating pruning method based on the impurity complexity measure. COMPSTAT '98. Proceedings in Computational Statistics, pp. 221-226.
4. Chaudhuri, P., Lo. W.D., Loh, Y.H., and Yang, C.C. 1995. Generalized regression trees, Statistica Sinica, 5:641-666.
5. Cox, D.R. 1972. The analysis of multivariate binary data. Applied Statistics, 21:113-120.
6. Diggle, P.J., Liang, K.Y., and Zeger, S.L. 1994. Analysis of Longitudinal Data. Oxford: Clarendon Press.
7. Hand, D.J. 1997. Construction and Assessment of Classification Rules. Chichester, UK: Wiley
8. Hawkins, D.M. and Kass, G.V. 1982. Automatic interaction detection. In Topics in Applied Multivariate Analysis, D.H. Hawkins, (Ed.), Cambridge University Press, pp. 269-302.
9. Horton, N.J., and Lipsitz, S.R. 1999. Review of software to fit generalized estimating equation regression models. The American Statistician. 53:160-169.
10. Laird, N.M. and Ware, J.H. 1982. Random-effects models for longitudinal data. Biometrics, 38:963-974.
11. Larsen, D.R. and Speckman, P.L. 2004. Multivariate regression trees for analysis of abundance data. Biometrics. 60:543-549.
12. Liang, K.-Y. and Zeger, S.L. 1986. Longitudinal data analysis using generalized linear models, Biometrika. 73:13-22.
13. Loh, W.-Y. and Vanichsetakul, N. 1988. Tree-structured classification via generalized discriminant analysis (with discussion). Journal of the American Statistical Association, 83:715-728.
14. McCullagh, P. and Neider, J.A. 1983. Generalized Linear Models. Chapman and Hall.
15. Quinlan, J.R. 1986. Introduction of decision tree. Machine Learning, 1:81-106.
16. SAS Institute, Inc 2000. SAS Macro Language: Reference, Version 8. Cary NC: SAS Institute Inc.
17. Segal, M.R. 1992. Tree-structured methods for longitudinal data. Journal of the American Statistical Association, 87:407-418.
18. Stokes, M.E., Davis, C.S., and Koch, G.G. 1995. Categorical Data Analysis Using the SAS System. Cary, NC: SAS Institute Inc.
19. Taylor, P.C. and Siverman, B.W. 1992. Block diagram and splitting criteria for classification trees. Statist. Comput., 3:147-161.
20. Thall, P.F. and Vail, S.C. 1990. Some covariance models for longitudinal count data with overdispersion. Biometrics, 46:657-671.
21. Ware M., Frank, E., Holmes, G., Hall, M., and Witten, I.H. 2001. Interactive machine learning: Letting users build clssifiers. Int. J. Human-Computer Studies, 55:281-292.
22. Yu, Y. and Lambert, D. 1999. Fitting trees to functional data, with an application to time-of-day patterns. Journal of Computational and Graphical Statistics, 8:749-762.
23. Zeger, S. and Liang, L. 1986. Longitudinal data analysis using generalized linear models. Biometrika, 73:13-22.
24. Zhang, H.P. 1998. Classification tree for multiple binary responses. Journal of the American Statistical Association, 93:180-193.
25. Zhang, H.P. and Singer, B. 1999. Recursive Partitioning in the Health Sciences. New York: Springer-Verlag.
26. Zhao, L.P., and Prentice, R.L. 1990. Correlated binary regression using a quadratic exponential model. Biometrika, 77:642-648.