Open Access
2016 Estimation of high-dimensional graphical models using regularized score matching
Lina Lin, Mathias Drton, Ali Shojaie
Electron. J. Statist. 10(1): 806-854 (2016). DOI: 10.1214/16-EJS1126


Graphical models are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating undirected conditional independence graphs based on the score matching loss, introduced by Hyvärinen (2005), and subsequently extended in Hyvärinen (2007). The regularized score matching method we propose applies to settings with continuous observations and allows for computationally efficient treatment of possibly non-Gaussian exponential family models. In the well-explored Gaussian setting, regularized score matching avoids issues of asymmetry that arise when applying the technique of neighborhood selection, and compared to existing methods that directly yield symmetric estimates, the score matching approach has the advantage that the considered loss is quadratic and gives piecewise linear solution paths under $\ell_{1}$ regularization. Under suitable irrepresentability conditions, we show that $\ell_{1}$-regularized score matching is consistent for graph estimation in sparse high-dimensional settings. Through numerical experiments and an application to RNAseq data, we confirm that regularized score matching achieves state-of-the-art performance in the Gaussian case and provides a valuable tool for computationally efficient estimation in non-Gaussian graphical models.


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Lina Lin. Mathias Drton. Ali Shojaie. "Estimation of high-dimensional graphical models using regularized score matching." Electron. J. Statist. 10 (1) 806 - 854, 2016.


Received: 1 September 2015; Published: 2016
First available in Project Euclid: 6 April 2016

zbMATH: 1336.62130
MathSciNet: MR3486418
Digital Object Identifier: 10.1214/16-EJS1126

Primary: 62H12
Secondary: 62F12

Keywords: Conditional independence graph , exponential family , Graphical model , High-dimensional statistics , score matching , Sparsity

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.10 • No. 1 • 2016
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