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• W3205070830 abstract "There has been considerable advance in understanding the properties of sparse regularization procedures in high‐dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector‐autoregressive models with heavy tailed, weakly dependent innovations. In contrast to current literature, our innovation process satisfy an L 1 mixingale type condition on the centered conditional covariance matrices. This condition covers L 1 ‐NED sequences and strong (‐) mixing sequences as particular examples." @default.
• W3205070830 created "2021-10-25" @default.
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• W3205070830 date "2021-10-26" @default.
• W3205070830 modified "2023-10-16" @default.
• W3205070830 title "Regularized estimation of high‐dimensional vector autoregressions with weakly dependent innovations" @default.
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• W3205070830 doi "https://doi.org/10.1111/jtsa.12627" @default.
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