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Regression trees and forests for non-homogeneous Poisson processes

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  • Mathlouthi, Walid
  • Fredette, Marc
  • Larocque, Denis

Abstract

We propose tree and random forest methods for non-homogeneous Poisson processes. The splitting criterion is derived from a model with a piecewise constant rate function. A simulation study shows that the new method performs well.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:96:y:2015:i:c:p:204-211
    DOI: 10.1016/j.spl.2014.09.025
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    References listed on IDEAS

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    1. Seong-Keon Lee & Seohoon Jin, 2006. "Decision tree approaches for zero-inflated count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(8), pages 853-865.
    2. Choi, Yunhee & Ahn, Hongshik & Chen, James J., 2005. "Regression trees for analysis of count data with extra Poisson variation," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 893-915, June.
    3. Biau, Gérard & Devroye, Luc, 2010. "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2499-2518, November.
    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.
    Full references (including those not matched with items on IDEAS)

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