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. 2020 Jan 27;10:1233. doi: 10.1038/s41598-019-56444-5

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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In silico study under the high-dimensional setting $(n_{t} < p' where p' = p (p + 1) / 2,$ total number of free parameters). According to the AUROC estimates, the proposed model DynGLasso performs better than GLasso under the high-dimensional setting. This plot shows the kernel density estimate of AUROCs from 20 distinct time-series datasets. When the sample size is large, the BIC estimates converge to the estimates with max AUROC. The max AUROC is the maximum AUROC computed over the hyperparameter space via grid search.