Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
arXiv 19 Dec 2019 · Mathematics — Statistics Theory · publishedJournal of Time Series Analysis (2021) · 7 citations (OpenAlex)
arXiv:1912.09002 · PDF · DOI · OpenAlex · Extracted main text
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 with virtually no assumption on the conditional heteroskedasticity. 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 ($\alpha$-) mixing sequences as particular examples.
appendix boundary found by appendix_command · 58% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Basu, S. and Michailidis, G (2015) Regularized estimation in sparse high-dimensional time series models | 1.000 | 5 | 3 | 100% |
| 2 | Wong, K., Li, Z., and Tewari, A (2020) Lasso guarantees for $$-mixing heavy tailed time series | 0.888 | 10 | 7 | 70% |
| 3 | Negahban, S. N., Ravikumar, P., Wainwright, M. J., and Yu, B (2012) A unified framework for high-dimensional analysis of $ m $-estimators with decomposable regularizers | 0.855 | 8 | 4 | 62% |
| 4 | Kock, A. and Callot, L (2015) Oracle inequalities for high dimensional vector autoregressions | 0.843 | 3 | 3 | 100% |
| 5 | Adamek, R., Smeekes, S., and Wilms, I (2020) Lasso inference for high-dimensional time series | 0.811 | 4 | 2 | 100% |
| 6 | Andrews, D. W (1988) Laws of large numbers for dependent non-identically distributed random variables | 0.644 | 2 | 2 | 100% |
| 7 | Merlevède, F., Peligrad, M., and Rio, E (2011) A bernstein type inequality and moderate deviations for weakly dependent sequences | 0.644 | 2 | 2 | 100% |
| 8 | Davidson, J (1994) Stochastic Limit Theory | 0.644 | 2 | 2 | 100% |
| 9 | Medeiros, M. and Mendes, E (2016) $_1$-regularization of high-dimensional time-series models with non-gaussian and heteroskedastic innovations self | 0.644 | 2 | 2 | 100% |
| 10 | Loh, P.-L. and Wainwright, M (2012) High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.