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https://doi.org/10.1198/016214507000000509
Title: | Unified LASSO estimation by least squares approximation | Authors: | Wang, H. Leng, C. |
Keywords: | Adaptive LASSO Bayes information criterion LASSO Least angle regression Least squares approximation Microarray data Oracle property Solution path |
Issue Date: | Sep-2007 | Citation: | Wang, H., Leng, C. (2007-09). Unified LASSO estimation by least squares approximation. Journal of the American Statistical Association 102 (479) : 1039-1048. ScholarBank@NUS Repository. https://doi.org/10.1198/016214507000000509 | Abstract: | We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical framework includes ordinary least squares, generalized linear models, quantile regression, and many others as special cases. Specifically, LSA can transfer many different types of LASSO objective functions into their asymptotically equivalent least squares problems. Thereafter, the standard asymptotic theory can be established and the LARS algorithm can be applied. In particular, if the adaptive LASSO penalty and a Bayes information criterion-type tuning parameter selector are used, the resulting LSA estimator can be as efficient as the oracle. Extensive numerical studies confirm our theory. © 2007 American Statistical Association. | Source Title: | Journal of the American Statistical Association | URI: | http://scholarbank.nus.edu.sg/handle/10635/105451 | ISSN: | 01621459 | DOI: | 10.1198/016214507000000509 |
Appears in Collections: | Staff Publications |
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