2023/06/30 by Yusuke Saigusa, Shinto Eguchi, Saigusa, Yusuke +3
Economics, Econometrics and Finance · Mathematics · #Economic and Environmental Valuation #FOS: Computer and information sciences #Methodology (stat.ME) #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2306.17386
openalex publication_date 2023/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.