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Selection of tuning parameters in bridge regression models via Bayesian information criterion

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  • Shuichi Kawano

Abstract

We consider bridge regression models, which can produce a sparse or non-sparse model by controlling a tuning parameter in the penalty term. A crucial part of a model building strategy is the selection of the values for adjusted parameters, such as regularization and tuning parameters. Indeed, this can be viewed as a problem in selecting and evaluating the model. We propose a Bayesian selection criterion for evaluating bridge regression models. This criterion enables us to objectively select the values of the adjusted parameters. We investigate the effectiveness of our proposed modeling strategy with some numerical examples. Copyright Springer-Verlag Berlin Heidelberg 2014

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  • Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:4:p:1207-1223
    DOI: 10.1007/s00362-013-0561-7
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    References listed on IDEAS

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    2. Yongxin Liu & Peng Zeng & Lu Lin, 2021. "Degrees of freedom for regularized regression with Huber loss and linear constraints," Statistical Papers, Springer, vol. 62(5), pages 2383-2405, October.

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