報告題目：Adaptive banding covariance estimation for high-dimensional multivariate longitudinal data
報 告 人：張偉平教授 中國科學技術大學
報告時間：2020年6月23日 上午 10:20-11:20
報告地點：騰訊會議
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會議 ID：367 304 956
會議密碼：200623
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：
Modeling the covariance matrix of multiple responses in longitudinal data plays a key role and is more challenging as compared to its univariate counterpart due to the presence of correlations among multiple responses. Using the modified Cholesky block decomposition, we impose an adaptive block banded structure on the Cholesky factor and sparsity on the innovation variance matrices via a novel convex hierarchical penalty and lasso penalty, respectively. The resulting adaptive block banding regularized estimator is fully data-driven and has more flexibility than regular banding estimators. An efficient alternative convex optimization algorithm is developed using ADMM algorithms. The resulting estimators are also shown to converge in an optimal rate of Frobenius norm, and row specific support recovery is established for the precision matrix. Simulations and real data analysis show that the proposed estimator can be better able to reveal the banding sparsity pattern in the data.
報告人簡介：
張偉平，中國科學技術大學教授，博導。主要從事縱向數據分析、風險理論、統計學習等領域中的統計理論和應用研究工作，先後在國内外學術期刊發表論文50餘篇。主持了國家自然科學基金青年和面上項目、重點項目子課題等多個項目。曾獲安徽省自然科學優秀論文一等獎、安徽省教學成果獎特等獎等。擔任全國工業統計學教學研究會、中國商業統計學會、中國現場統計研究會環境與資源統計分會以及數據科學與人工智能分會等學會的理事。
