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- W2884561489 abstract "We begin by reviewing recent results of Ing and Lai (Stat Sin 21:1473–1513, 2011) on the statistical properties of the orthogonal greedy algorithm (OGA) in high-dimensional sparse regression models with independent observations. In particular, when the regression coefficients are absolutely summable, the conditional mean squared prediction error and the empirical norm of OGA derived by Ing and Lai (Stat Sin 21:1473–1513, 2011) are introduced. We then explore the performance of OGA under more general sparsity conditions. Finally, we obtain the convergence rate of OGA in high-dimensional time series models, and illustrate the advantage of our results compared to those established for Lasso by Basu and Michailidis (Ann Stat 43:1535–1567, 2015) and Wu and Wu (Electron J Stat 10:352–379, 2016)." @default.
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- W2884561489 date "2018-01-01" @default.
- W2884561489 modified "2023-09-27" @default.
- W2884561489 title "Analysis of High-Dimensional Regression Models Using Orthogonal Greedy Algorithms" @default.
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- W2884561489 doi "https://doi.org/10.1007/978-3-319-18284-1_10" @default.
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