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Joint estimation of multiple graphical models from high dimensional time series

Author(s): Qiu, Huitong; Han, Fang; Liu, Han; Caffo, Brian

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Abstract: We consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel‐based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide an explicit rate of convergence in parameter estimation. It characterizes the strength that one can borrow across different individuals and the effect of data dependence on parameter estimation. Empirically, experiments on both synthetic and real resting state functional magnetic resonance imaging data illustrate the effectiveness of the method proposed.
Publication Date: 2016
Citation: Qiu, H., Han, F., Liu, H. and Caffo, B. "Joint estimation of multiple graphical models from high dimensional time series." Journal of the Royal Statistical Society: Series B, 78, no. 2 (2016): 487-504. doi:10.1111/rssb.12123
DOI: doi:10.1111/rssb.12123
ISSN: 1369-7412
EISSN: 1467-9868
Pages: 487 - 504
Type of Material: Journal Article
Journal/Proceeding Title: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Version: Author's manuscript

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