Sparse modifying algorithm in Bayesian lasso(Session 4a)

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概要

• 論文の詳細を見る

• The lasso is simultaneous variable selection and parameter estimation procedure in linear regression models. The estimates can be interpreted as a Bayesian posterior mode when independent Laplace prior distributions are placed on the regression coefficients. Park and Casclla (2008) extended the Bayesian lasso linear regression model by placing prior distributions on hyperparameters in independent Laplace distribution. It might be however noted that the point estimate of Bayesian lasso is not sparse. In the present paper, we propose an efficient algorithm which modifies the Bayesian lasso estimates so as to be sparse. Monte Carlo simulations are conducted to investigate the efficiency of the proposed algorithm.

• 2011-11-11

著者

• Konishi Sadanori

  Faculty Of Mathematics Kyushu University

• Hoshina Ibuki

  Graduate School Of Science And Engineering Chuo University

関連論文

• VARIABLE SELECTION IN LOGISTIC DISCRIMINATION BASED ON LOCAL LIKELIHOOD
• LOGISTIC DISCRIMINATION BASED ON REGULARIZED LOCAL LIKELIHOOD METHOD
• Sparse modifying algorithm in Bayesian lasso(Session 4a)
• Tuning parameter selection for L_1 type regularization(Session 2b)

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