Structured learning of time-varying networks with application to PM_2.5 data

References

• Banerjee, O., L. E. Ghaoui, and A. d’Aspremont. 2008. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. Journal of Machine Learning Research 9:485–516. doi:10.1145/1390681.1390696.
   Web of Science ®Google Scholar
• Boyd, S., N. Parikh, E. Chu, B. Peleato, and J. Eckstein. 2010. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning 3 (1):1–122. doi:10.1561/2200000016.
   Google Scholar
• Clauset, A., M. E. J. Newman, and C. Moore. 2004. Finding community structure in very large networks. Physical Review E 70:485–516. doi:10.1103/PhysRevE.70.066111.
   Web of Science ®Google Scholar
• Danaher, P., P. Wang, and D. M. Witten. 2014. The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (2):373–97. doi:10.1111/rssb.12033.
   PubMed Web of Science ®Google Scholar
• Devijver, E., and M. Gallopin. 2018. Block-diagonal covariance selection for high-dimensional gaussian graphical models. Journal of the American Statistical Association 113 (521):306–314. doi:10.1080/01621459.2016.1247002.
   Web of Science ®Google Scholar
• Edwards, D. 2000. Introduction to graphical modelling. New York: Springer Verlag.
   Google Scholar
• Foygel, R., and M. Drton. 2010. Extended Bayesian information criteria for gaussian graphical models. In Advances in Neural Information Processing Systems 23:604–612.
   Google Scholar
• Friedman, J., T. Hastie, and R. Tibshirani. 2008. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9 (3):432–41. doi:10.1093/biostatistics/kxm045.
   PubMed Web of Science ®Google Scholar
• Guo, J., E. Levina, G. Michailidis, and J. Zhu. 2011. Joint estimation of multiple graphical models. Biometrika 98 (1):1–15. doi:10.1093/biomet/asq060.
   PubMed Web of Science ®Google Scholar
• Hallac, D., Y. Park, S. Boyd, and J. Leskovec. 2017. Network inference via the time-varying graphical lasso. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 205-213. doi:10.1145/3097983.3098037.
   Google Scholar
• Hosseini, M. J., and S.-I. Lee. 2016. Learning sparse gaussian graphical models with overlapping blocks. In Conference on Neural Information Processing Systems 30:3801–3809.
   Google Scholar
• Kolar, M., L. Song, A. Ahmed, and E. Xing. 2010. Estimating time-varying networks. The Annals of Applied Statistics 4 (1):94–123. doi:10.1214/09-AOAS308.
   Web of Science ®Google Scholar
• Lauritzen, S. 1996. Graphical models. Oxford: Oxford University Press.
   Google Scholar
• Liang, X., S. Li, S. Zhang, and S. X. Chen. 2016. PM2.5 data reliability, consistency and air quality asssessment in five Chinese cities. Journal of Geophysical Research: Atmospheres 121:10220–36. doi:10.1002/2016JD024877.
   Web of Science ®Google Scholar
• Liang, X., T. Zou, and B. Guo. 2015. Assessing Beijing’s PM2.5 pollution: severity, weather impact, Apec and winter heating. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 471 (2182):20150257. doi:10.1098/rspa.2015.0257.
   Google Scholar
• Pearl, J. 2000. Causality: models, reasoning, and inference. Cambridge: Cambridge University Press.
   Google Scholar
• Ravikumar, P., M. J. Wainwright, G. Raskutti, and B. Yu. 2011. High-dimensional covariance estimation by minimizing l1-penalized log-determinant. Electronic Journal of Statistics 5:935–80. doi:10.1214/11-EJS631.
   Web of Science ®Google Scholar
• Saegusa, T., and A. Shojaie. 2016. Joint estimation of precision matrices in heterogeneous populations. Electronic Journal of Statistics 10 (1):1341–92. doi:10.1214/16-EJS1137.
   PubMed Web of Science ®Google Scholar
• Tan, K. M., D. Witten, and A. Shojaie. 2015. The cluster graphical lasso for improved estimation of gaussian graphical models. Computational Statistics & Data Analysis 85:23–56. doi:10.1016/j.csda.2014.11.015.
   PubMed Web of Science ®Google Scholar
• Wit, E., and A. Abbruzzo. 2015. Inferring slowly-changing dynamic gene-regulatory networks. BMC Bioinformatics 16 (Suppl6):S5. doi:10.1186/1471-2105-16-S6-S5.
   PubMed Web of Science ®Google Scholar
• Yuan, M., and Y. Lin. 2007. Model selection and estimation in the gaussian graphical model. Biometrika 94 (1):19–35. doi:10.1093/biomet/asm018.
   Web of Science ®Google Scholar
• Zhou, S., J. Lafferty, and L. Wasserman. 2010. Time varying undirected graphs. Machine Learning 80 (2–3):295–319. doi:10.1007/s10994-010-5180-0.
   Web of Science ®Google Scholar