Model Selection and Estimation in Regression with Grouped Variables

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ID: 289311
2005
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Ranked #54 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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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.
Reference Key
openalex_W2138019504 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ming Yuan, Yi Lin
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2005
DOI
10.1111/j.1467-9868.2005.00532.x
URL
https://academic.oup.com/jrsssb/article-pdf/68/1/49/49794691/jrsssb_68_1_49.pdf
Keywords Keywords not found

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