Bayesian generalized fused lasso modeling via NEG distribution

Kaito Shimamura, Masao Ueki, Shuichi Kawano and Sadanori Konishi

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 16, 4132-4153

Abstract: The fused lasso penalizes a loss function by the L1 norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso using a flexible regularization term. Simulation studies and real data analyses show that the proposed method has superior performance to the ordinary fused lasso.

Date: 2019
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DOI: 10.1080/03610926.2018.1489056

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