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On generalized multivariate decision tree by using GEE

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  • Keon Lee, Seong

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  • Keon Lee, Seong, 2005. "On generalized multivariate decision tree by using GEE," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1105-1119, June.
    • Handle: RePEc:eee:csdana:v:49:y:2005:i:4:p:1105-1119
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    References listed on IDEAS

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    1. David R. Larsen & Paul L. Speckman, 2004. "Multivariate Regression Trees for Analysis of Abundance Data," Biometrics, The International Biometric Society, vol. 60(2), pages 543-549, June.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    3. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
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    Cited by:

    1. Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    2. Hsiao, Wei-Cheng & Shih, Yu-Shan, 2007. "Splitting variable selection for multivariate regression trees," Statistics & Probability Letters, Elsevier, vol. 77(3), pages 265-271, February.
    3. Mathlouthi, Walid & Fredette, Marc & Larocque, Denis, 2015. "Regression trees and forests for non-homogeneous Poisson processes," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 204-211.
    4. Hajjem, Ahlem & Bellavance, François & Larocque, Denis, 2011. "Mixed effects regression trees for clustered data," Statistics & Probability Letters, Elsevier, vol. 81(4), pages 451-459, April.
    5. Schmid, Lena & Gerharz, Alexander & Groll, Andreas & Pauly, Markus, 2023. "Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    6. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    7. Peter Calhoun & Richard A. Levine & Juanjuan Fan, 2021. "Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia," Biometrics, The International Biometric Society, vol. 77(1), pages 343-351, March.

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