XGBoostPP: Tree-based Estimation of Point Process Intensity Functions

Changqing Lu, Yongtao Guan*, Marie-Colette van Lieshout, Ganggang Xu

*Corresponding author for this work

Research output: Working paperPreprintAcademic

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Abstract

We propose a novel tree-based ensemble method, named XGBoostPP, to nonparametrically estimate the intensity of a point process as a function of covariates. It extends the use of gradient-boosted regression trees (Chen & Guestrin, 2016) to the point process literature via two carefully designed loss functions. The first loss is based on the Poisson likelihood, working for general point processes. The second loss is based on the weighted Poisson likelihood, where spatially dependent weights are introduced to further improve the estimation efficiency for clustered processes. An efficient greedy search algorithm is developed for model estimation, and the effectiveness of the proposed method is demonstrated through extensive simulation studies and two real data analyses. In particular, we report that XGBoostPP achieves superior performance to existing approaches when the dimension of the covariate space is high, revealing the advantages of tree-based ensemble methods in estimating complex intensity functions
Original languageEnglish
PublisherArXiv.org
Number of pages21
DOIs
Publication statusPublished - 31 Jan 2024

Keywords

  • Point process
  • Nonparametric intensity estimation
  • Tree-based ensemble method
  • XGBoost

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