Score matching is an estimation procedure that has been developed for statistical models whose probability density function is known up to proportionality but whose normalizing constant is intractable, so that maximum likelihood is difficult or impossible to implement. To date, applications of score matching have focused more on continuous IID models. Motivated by various data modelling problems, this article proposes a unified asymptotic theory of generalized score matching developed under the independence assumption, covering both continuous and discrete response data, thereby giving a sound basis for score-matchingbased inference. Real data analyses and simulation studies provide convincing evidence of strong practical performance of the proposed methods.

翻译：得分匹配是一种估计方法，专为概率密度函数已知（至比例常数）但归一化常数难以处理的统计模型而开发，这使得最大似然估计难以甚至无法实现。迄今为止，得分匹配的应用主要集中于连续的独立同分布模型。受多种数据建模问题的驱动，本文提出了在独立性假设下发展的广义得分匹配的统一渐近理论，该理论涵盖了连续和离散响应数据，从而为基于得分匹配的推断奠定了坚实的基础。实际数据分析和模拟研究提供了令人信服的证据，证明了所提出方法在实践中的强大性能。