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. For such models, maximum likelihood estimation will be difficult or impossible to implement. To date, nearly all applications of score matching have focused on continuous IID (independent and identically distributed) models. Motivated by various data modelling problems for which the continuity assumption and/or the IID assumption are not appropriate, this article proposes three novel extensions of score matching: (i) to univariate and multivariate ordinal data (including count data); (ii) to INID (independent but not necessarily identically distributed) data models, including regression models with either a continuous or a discrete ordinal response; and (iii) to a class of dependent data models known as auto models. Under the INID assumption, a unified asymptotic approach to settings (i) and (ii) is developed and, under mild regularity conditions, it is proved that the proposed score matching estimators are consistent and asymptotically normal. These theoretical results provide a sound basis for score-matching-based inference and are supported by strong performance in simulation studies and a real data example involving doctoral publication data. Regarding (iii), motivated by a spatial geochemical dataset, we develop a novel auto model for spatially dependent spherical data and propose a score-matching-based Wald statistic to test for the presence of spatial dependence. Our proposed auto model exhibits a way to model spatial dependence of directions, is computationally convenient to use and is expected to be superior to composite likelihood approaches for reasons that are explained.
翻译:计分匹配是针对概率密度函数为相称性而已知但正常化的常态难以实现的统计模型开发的一种估算程序。对于这些模型来说,最大概率估算将很难或不可能实施。迄今为止,几乎所有得分匹配应用都侧重于连续的 IID(独立和完全分布)模型。受连续假设和/或ID假设不适当的各种数据建模问题的影响,本条提议了三个新颖的得分匹配扩展:(一) 至单数和多变量或多变量数据(包括计数数据);(二) 至INID(独立但不一定相同分布的)数据模型,包括连续或离散或非直接反应的回归模型;(三) 几乎所有得分匹配应用都侧重于连续的 IID(独立和相同分布的分布)模型。根据INID假设,对环境(一) 和(二) 开发了统一的零点测试方法,在较轻的周期性模型条件下,拟议的与估计的度比值计算方法是一致和正常的。这些理论结果为以连续或连续或连续分布的回归方法提供了精确的回归模型基础,用于进行评分数据模拟的模拟数据测试,并且通过模拟进行模拟的模拟的模拟数据模拟数据模拟的模拟的模拟数据模拟的模拟的模拟的模拟数据模拟的模拟的模拟的模拟的模拟的模拟的模拟数据。</s>