Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models

First Page		Document Content
Date: 2008-07-18 23:29:17 Statistical theory Econometrics Decision theory Elastic net regularization Least squares Tikhonov regularization Least-angle regression Expectation–maximization algorithm Lasso Statistics Estimation theory Regression analysis		Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models Add to Reading List Source URL: www.personal.psu.edu Download Document from Source Website File Size: 70,08 KB Share Document on Facebook

	JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ DocID: 1qZSe - View Document
	Why does least angle regression work? Sven Laur Helsinki University of Technology Minimisation goal of the LASSO algorithm DocID: 1ktMm - View Document
	LARS Library: Least Angle Regression Stagewise Library Frank Vanden Berghen IRIDIA, Universit´e Libre de Bruxelles November 22, 2005 DocID: 1eQhj - View Document
	Iterative Learning: Leveraging the Computer as an On-Demand Expert Artist Tim Althoff∗ University of California, Berkeley (a) Original Image DocID: 19x1A - View Document
	Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models DocID: 18TxC - View Document