Model selection via adaptive shrinkage with t priors |
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Authors: | Artin Armagan Russell L Zaretzki |
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Institution: | (1) Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA;(2) International Maize and Wheat Improvement Center, 06600 Mexico, DF Mexico |
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Abstract: | We discuss a model selection procedure, the adaptive ridge selector, derived from a hierarchical Bayes argument, which results in a simple and efficient fitting algorithm. The hierarchical
model utilized resembles an un-replicated variance components model and leads to weighting of the covariates. We discuss the
intuition behind this type estimator and investigate its behavior as a regularized least squares procedure. While related
alternatives were recently exploited to simultaneously fit and select variablses/features in regression models (Tipping in
J Mach Learn Res 1:211–244, 2001; Figueiredo in IEEE Trans Pattern Anal Mach Intell 25:1150–1159, 2003), the extension presented
here shows considerable improvement in model selection accuracy in several important cases. We also compare this estimator’s
model selection performance to those offered by the lasso and adaptive lasso solution paths. Under randomized experimentation,
we show that a fixed choice of tuning parameter leads to results in terms of model selection accuracy which are superior to
the entire solution paths of lasso and adaptive lasso when the underlying model is a sparse one. We provide a robust version
of the algorithm which is suitable in cases where outliers may exist. |
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