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Model Selection Consistency of Lasso for...

Yang, Yuehan...

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Model Selection Consistency of Lasso for Empirical Data by Yang, Yuehan ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	07-09-2023
Summary	Large-scale empirical data, the sample size and the dimension are high, often exhibit various characteristics. For example, the noise term follows unknown distributions or the model is very sparse that the number of critical variables is fixed while dimensionality grows with $n$. We consider the model selection problem of lasso for this kind of data. We investigate both theoretical guarantees and simulations, and show that the lasso is robust for various kinds of data.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1502.01798

Citation

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AMA 11th EditionReferences

Yang, Yuehan. Model Selection Consistency of Lasso for Empirical Data. Australian National University. September 07, 2023. Accessed November 02, 2024. https://online.provdatabase.com/cite/24366

APA 7th EditionReferences

Yang, Yuehan. (2023). Model Selection Consistency of Lasso for Empirical Data. Australian National University. https://online.provdatabase.com/cite/24366

Chicago 17th EditionReferences

Yang, Yuehan. Model Selection Consistency of Lasso for Empirical Data. Australian National University. 2023. https://online.provdatabase.com/cite/24366

HarvardReferences

Yang, Yuehan. (2023). Model Selection Consistency of Lasso for Empirical Data. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/24366. (Accessed 02 November 2024).

Harvard AustraliaReferences

Yang, Yuehan, 2023, Model Selection Consistency of Lasso for Empirical Data, Australian National University, viewed 02 November 2024, <https://online.provdatabase.com/cite/24366>

MLA 9th EditionWorks Cited

Yang, Yuehan. Model Selection Consistency of Lasso for Empirical Data. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/24366.

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