Abstract
Many scientific and economic problems involve the analysis of high-dimensional time series datasets. However, theoretical studies in high-dimensional statistics to date rely primarily on the assumption of independent and identically distributed (i.i.d.) samples. In this work, we focus on stable Gaussian processes and investigate the theoretical properties of
Citation
Sumanta Basu. George Michailidis. "Regularized estimation in sparse high-dimensional time series models." Ann. Statist. 43 (4) 1535 - 1567, August 2015. https://doi.org/10.1214/15-AOS1315
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