PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

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  • Kawano Shuichi
    Graduate School of Information Systems, The University of Electro-Communications
  • Hoshina Ibuki
    Department of Mathematics, Graduate School of Science and Engineering, Chuo University
  • Shimamura Kaito
    Department of Mathematics, Faculty of Science and Engineering, Chuo University
  • Konishi Sadanori
    Department of Mathematics, Faculty of Science and Engineering, Chuo University

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<p>We consider the Bayesian lasso for regression, which can be interpreted as an L1 norm regularization based on a Bayesian approach when the Laplace or doubleexponential prior distribution is placed on the regression coefficients. A crucial issue is an appropriate choice of the values of hyperparameters included in the prior distributions, which essentially control the sparsity in the estimated model. To choose the values of tuning parameters, we introduce a model selection criterion for evaluating a Bayesian predictive distribution for the Bayesian lasso. Numerical results are presented to illustrate the properties of our sparse Bayesian modeling procedure.</p>

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