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We developed a new hurdle methodology using two random forests to accurately model count data with excess zeros. This approach improves predictions for both homogeneous and non-homogeneous Poisson processes, outperforming existing methods.

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Hurdle modelPoisson processnon-homogeneous Poisson processrandom foreststree-based methodzero-altered Poisson (ZAP)zero-inflated Poisson (ZIP)

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Poisson processes are common for count data, but often exhibit excess zeros.
  • Existing models may struggle with both excess zeros and complex process variations (homogeneous/non-homogeneous).

Purpose of the Study:

  • To introduce a novel hurdle methodology for modeling count data with excess zeros.
  • To enhance the modeling of homogeneous and non-homogeneous Poisson processes.

Main Methods:

  • Utilized a two-forest approach: one for zero probability, one for Poisson parameters.
  • Developed specialized splitting criteria for the second forest based on zero-truncated Poisson likelihood.
  • Applied the method to homogeneous and non-homogeneous Poisson processes.

Main Results:

  • The proposed hurdle methodology effectively models count data with excess zeros.
  • Demonstrated superior performance compared to existing methods in simulations.
  • Showed robustness in both hurdle (zero-altered) and zero-inflated scenarios.

Conclusions:

  • The two-forest hurdle methodology provides a flexible and accurate tool for count data analysis.
  • The method is effective for both homogeneous and non-homogeneous Poisson processes with excess zeros.
  • Successfully applied to real-world data on elderly healthcare demand.