Penalized Regressions: The Bridge versus the Lasso |
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Authors: | Wenjiang J Fu |
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Institution: | Department of Epidemiology , Michigan State University , East Lansing , MI , 48823 , USA |
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Abstract: | Abstract Bridge regression, a special family of penalized regressions of a penalty function Σ|βj|γ with γ ≤ 1, considered. A general approach to solve for the bridge estimator is developed. A new algorithm for the lasso (γ = 1) is obtained by studying the structure of the bridge estimators. The shrinkage parameter γ and the tuning parameter λ are selected via generalized cross-validation (GCV). Comparison between the bridge model (γ ≤ 1) and several other shrinkage models, namely the ordinary least squares regression (λ = 0), the lasso (γ = 1) and ridge regression (γ = 2), is made through a simulation study. It is shown that the bridge regression performs well compared to the lasso and ridge regression. These methods are demonstrated through an analysis of a prostate cancer data. Some computational advantages and limitations are discussed. |
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Keywords: | Bayesian prior Bridge regressions GCV Newton-Raphson Shrinkage Shooting method |
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