Model selection and estimation in regression with grouped variables Export

@article{yuan2006regression, abstract = {Summary. We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multifactor analysis-of-variance problem as the most important and well-known example. Instead of selecting factors by stepwise backward elimination, we focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection. The lasso, the LARS algorithm and the non-negative garrotte are recently proposed regression methods that can be used to select individual variables. We study and propose efficient algorithms for the extensions of these methods for factor selection and show that these extensions give superior performance to the traditional stepwise backward elimination method in factor selection problems. We study the similarities and the differences between these methods. Simulations and real examples are used to illustrate the methods.}, address = {Georgia Institute of Technology, Atlanta, USA; University of WisconsinMadison, USA}, author = {Yuan, Ming and Lin, Yi}, citeulike-article-id = {448082}, citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2005.00532.x}, citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1467-9868.2005.00532.x}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/bpl/rssb/2006/00000068/00000001/art00004}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/118566968/ABSTRACT}, doi = {10.1111/j.1467-9868.2005.00532.x}, issn = {1467-9868}, journal = {Journal of the Royal Statistical Society Series B}, keywords = {covariance, glasso, inverse, model, selection}, month = {February}, number = {1}, pages = {49--67}, posted-at = {2009-10-30 20:10:19}, priority = {2}, publisher = {Blackwell Publishing}, title = {Model selection and estimation in regression with grouped variables}, url = {http://dx.doi.org/10.1111/j.1467-9868.2005.00532.x}, volume = {68}, year = {2006} }

TY - JOUR ID - yuan2006regression L3 - citeulike-article-id:448082 N2 - Summary. We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multifactor analysis-of-variance problem as the most important and well-known example. Instead of selecting factors by stepwise backward elimination, we focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection. The lasso, the LARS algorithm and the non-negative garrotte are recently proposed regression methods that can be used to select individual variables. We study and propose efficient algorithms for the extensions of these methods for factor selection and show that these extensions give superior performance to the traditional stepwise backward elimination method in factor selection problems. We study the similarities and the differences between these methods. Simulations and real examples are used to illustrate the methods. IS - 1 JF - Journal of the Royal Statistical Society Series B SN - 1467-9868 AD - Georgia Institute of Technology, Atlanta, USA; University of WisconsinMadison, USA EP - 67 TI - Model selection and estimation in regression with grouped variables PB - Blackwell Publishing VL - 68 SP - 49 KW - covariance KW - glasso KW - inverse KW - model KW - selection AU - Yuan, Ming AU - Lin, Yi PY - 2006/02// UR - http://dx.doi.org/10.1111/j.1467-9868.2005.00532.x ER -