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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
关键字：Sparsistency high dimensionality dynamic graphical models varying coefficient Kernel smoother
摘要：In the paper, we propose a class of dynamic conditional Gaussian graphical model (DCGGMs) based on a set of nonidentical distribution observations, which changes smoothly with time or condition. Specifically, the DCGGMs model the dynamic output network influenced by conditioning input variables, which are encoded by a set of varying parameters. Moreover, we propose a joint smooth graphical Lasso to estimate the DCGGMs, which combines kernel smoother with sparse group Lasso penalty. At the same time, we design an efficient accelerated proximal gradient algorithm to solve this estimator. Theoretically, we establish the asymptotic properties of our model on consistency and sparsistency under the high-dimensional settings. In particular, we highlight a class of consistency theory for dynamic graphical models, in which the sample size can be seen as n(4/5) for estimating a local graphical model when the bandwidth parameter h of kernel smoother is chosen as h asymptotic to n(-1/5) for describing the dynamic. Finally, the extensive numerical experiments on both synthetic and real datasets are provided to support the effectiveness of the proposed method.
ISSN号：1041-4347
是否译文：否
发表时间：2018-04-01
合写作者：陈松灿
通讯作者：黄飞虎