@article{oai:uec.repo.nii.ac.jp:00008846,
 author = {Shimamura, Kaito and Ueki, Masao and Kawano, Shuichi and Konishi, Sadanori},
 issue = {16},
 journal = {Communications in Statistics - Theory and Methods},
 month = {Nov},
 note = {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.},
 pages = {4132--4153},
 title = {Bayesian generalized fused lasso modeling via NEG distribution},
 volume = {48},
 year = {2018}
}