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• W3049332904 abstract "We investigate the prediction capability of the orthogonal greedy algorithm (OGA) in high-dimensional regression models with dependent observations. The rates of convergence of the prediction error of OGA are obtained under a variety of sparsity conditions. To prevent OGA from overfitting, we introduce a high-dimensional Akaike’s information criterion (HDAIC) to determine the number of OGA iterations. A key contribution of this work is to show that OGA, used in conjunction with HDAIC, can achieve the optimal convergence rate without knowledge of how sparse the underlying high-dimensional model is." @default.
• W3049332904 created "2020-08-21" @default.
• W3049332904 creator A5005084856 @default.
• W3049332904 date "2020-08-01" @default.
• W3049332904 modified "2023-09-25" @default.
• W3049332904 title "Model selection for high-dimensional linear regression with dependent observations" @default.
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• W3049332904 doi "https://doi.org/10.1214/19-aos1872" @default.
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