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Multivariate trees for mixed outcomes

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  • Dine, Abdessamad
  • Larocque, Denis
  • Bellavance, François

Abstract

In this paper, we propose a tree-based method for multivariate outcomes consisting in a mixture of categorical and continuous responses. The split function for tree-growing is derived from a likelihood-based approach for a general location model (GLOM). One situation where the new approach should be appealing is when mixed types of multiple outcomes are used as surrogates for an unobserved latent outcome. Two illustrations of the application of the new method are given with health care data.

Suggested Citation

  • Dine, Abdessamad & Larocque, Denis & Bellavance, François, 2009. "Multivariate trees for mixed outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3795-3804, September.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:11:p:3795-3804
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    References listed on IDEAS

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    1. Diana L. Miglioretti, 2003. "Latent Transition Regression for Mixed Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 710-720, September.
    2. Fan, Juanjuan & Su, Xiao-Gang & Levine, Richard A. & Nunn, Martha E. & LeBlanc, Michael, 2006. "Trees for Correlated Survival Data by Goodness of Split, With Applications to Tooth Prognosis," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 959-967, September.
    3. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
    4. Keon Lee, Seong, 2005. "On generalized multivariate decision tree by using GEE," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1105-1119, June.
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    1. 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).
    2. Piccarreta, Raffaella, 2010. "Binary trees for dissimilarity data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1516-1524, June.

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