Robust minimum divergence estimation in a spatial Poisson point process

TL;DR

This paper introduces a robust estimation method for spatial Poisson point processes in species distribution modeling, improving predictive accuracy under model misspecification and heterogeneous data conditions.

Contribution

It proposes a novel divergence-based estimation technique that enhances robustness over traditional likelihood methods in SDM applications.

Findings

Improved predictive performance in simulations

Effective mitigation of heterogeneity in real data

Enhanced robustness against model misspecification

Abstract

Species distribution modeling (SDM) plays a crucial role in investigating habitat suitability and addressing various ecological issues. While likelihood analysis is commonly used to draw ecological conclusions, it has been observed that its statistical performance is not robust when faced with slight deviations due to misspecification in SDM. We propose a new robust estimation method based on a novel divergence for the Poisson point process model. The proposed method is characterized by weighting the log-likelihood equation to mitigate the impact of heterogeneous observations in the presence-only data, which can result from model misspecification. We demonstrate that the proposed method improves the predictive performance of the maximum likelihood estimation in our simulation studies and in the analysis of vascular plant data in Japan.