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Splitting variable selection for multivariate regression trees

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  • Hsiao, Wei-Cheng
  • Shih, Yu-Shan

Abstract

We show that the usual exhaustive search principle adapted for multivariate regression trees has selection bias toward variables with more split points. A selection scheme is proposed to control bias by utilizing hierarchical loglinear model for three-way contingency table of residuals.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:3:p:265-271
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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. 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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    Cited by:

    1. Yu-Shan Shih & Kuang-Hsun Liu, 2019. "Regression trees for detecting preference patterns from rank data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 683-702, September.
    2. 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).
    3. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.

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    Keywords

    Bias Loglinear model Residual;

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