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Innovated scalable dynamic learning for time-varying graphical models

Author

Listed:
  • Zheng, Zemin
  • Li, Liwan
  • Zhou, Jia
  • Kong, Yinfei

Abstract

In this paper, we propose a new approach of innovated scalable dynamic learning (ISDL) for estimating time-varying graphical structures. Motivated by the innovated transformation, we convert the original problem into large covariance matrix estimation and exploit the scaled Lasso with kernel smoothing to simplify the tuning procedure. In addition, we show that our method has theoretical guarantees under mild regularity conditions for accurate estimation of each precision matrix.

Suggested Citation

  • Zheng, Zemin & Li, Liwan & Zhou, Jia & Kong, Yinfei, 2020. "Innovated scalable dynamic learning for time-varying graphical models," Statistics & Probability Letters, Elsevier, vol. 165(C).
    • Handle: RePEc:eee:stapro:v:165:y:2020:i:c:s0167715220301462
    DOI: 10.1016/j.spl.2020.108843
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    References listed on IDEAS

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    1. Peng, Jie & Wang, Pei & Zhou, Nengfeng & Zhu, Ji, 2009. "Partial Correlation Estimation by Joint Sparse Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 735-746.
    2. Teng Zhang & Hui Zou, 2014. "Sparse precision matrix estimation via lasso penalized D-trace loss," Biometrika, Biometrika Trust, vol. 101(1), pages 103-120.
    3. Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
    4. Liu, Weidong & Luo, Xi, 2015. "Fast and adaptive sparse precision matrix estimation in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 153-162.
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