Work

Generalized score matching

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Multivariate Analysis

DOI: 10.1016/j.jmva.2025.105473

Authors: Jiazhen Xu, Janice L. Scealy, Andrew T.A. Wood, Tao Zou

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,310

Funders: National Computational Infrastructure, Australian National University, Australian Research Council

Award: Novel statistical methods for data with non-Euclidean geometric structure