Generalized Score Matching for Non-Negative Data
Year: 2018
Type: article
Abstract: . In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. As an example, we consider a general class of pairwise interaction models. Addressing an overlooked inexistence problem, we generalize the regularized score matching method of Lin et al. (2016) and improve its theoretical guarantees for non-negative Gaussian graphical models.
Source: PubMed
Authors: Yu, Shiqing, Drton, Mathias, Shojaie, Ali
Institutions: University of Washington, University of Copenhagen
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological Data, Machine Learning Tools for Discovery and Analysis of Active Metabolic Pathways, CAREER: High-Dimensional Variable Selection in Nonlinear Models and Classification with Correlated Data, Statistical Methods for Network-Based Integrative Analysis of CVD Epigenetic Data, G6671297155, G702902909
OpenAlex ID: W2908411392