Group for Research in Decision Analysis


Random forests for non-homogeneous Poisson processes with excess zeros

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We propose a method to build trees and forests when the response is a non-homogeneous Poisson process with excess zeros, based on two forests. The first one is used to estimate the probability of having a zero. The second forest is used to estimate the Poisson parameter using trees built with a splitting criterion derived from the zero truncated non-homogeneous Poisson likelihood. Simulation studies show that the proposed method performs well in hurdle (zero-altered) and zero-inflated settings.

, 16 pages