Generalized score matching
提出一种广义得分匹配的统一渐近理论，适用于连续和离散响应数据，为基于得分匹配的推断提供理论基础，并通过实际数据和模拟验证其良好性能。
Score matching is an estimation procedure that has been developed for statistical models whose probability density function or probability mass 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 modeling 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-matching-based inference. Real data analyses and simulation studies provide convincing evidence of strong practical performance of the proposed methods.