A0691
Title: Is score matching suitable for estimating point processes
Authors: Feng Zhou - Renmin University of China (China) [presenting]
Abstract: Score-matching estimators for point processes have gained widespread attention in recent years because they do not require the calculation of intensity integrals, thereby effectively addressing the computational challenges in maximum likelihood estimation (MLE). Some existing works have proposed score-matching estimators for point processes. However, it is demonstrated that the incompleteness of the estimators proposed in those works renders them applicable only to specific problems, and they fail for more general point processes. To address this issue, the weighted score matching estimator is introduced to point processes. Theoretically, the consistency of the estimator proposed is proven. Experimental results indicate that the estimator accurately estimates model parameters on synthetic data and yields results consistent with MLE on real data. In contrast, existing score-matching estimators fail to perform effectively.
