High-dimensional inference for linear model with correlated errors
Author:
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s00184-021-00820-7.pdf
Reference37 articles.
1. Adamek R, Smeekes S, Wilms I (2020) Lasso inference for high-dimensional time series. arXiv Pre-print arXiv:2007.10952v1
2. Babii A, Ghysels E, Striaukas J (2020) Inference for high-dimensional regressions with heteroskedasticity and autocorrelation. arXiv Pre-print arXiv:1912.06307v2
3. Basu S, Michailidis G (2015) Regularized estimation in sparse high-dimensional time series models. Ann Stat 43:1535–1567
4. Bickel PJ, Levina E (2008) Regularized estimation of large covariance matrices. Ann Stat 36:199–227
5. Bühlmann P, van de Geer S (2011) Statistics for high-dimensional data: methods, theory and applications. Springer, New York
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1. Inference for high‐dimensional linear models with locally stationary error processes;Journal of Time Series Analysis;2023-04-04
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