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Bayesian generalized fused lasso modeling via NEG distribution

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  • Kaito Shimamura
  • Masao Ueki
  • Shuichi Kawano
  • Sadanori Konishi

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.

Suggested Citation

  • Kaito Shimamura & Masao Ueki & Shuichi Kawano & Sadanori Konishi, 2019. "Bayesian generalized fused lasso modeling via NEG distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(16), pages 4132-4153, August.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:16:p:4132-4153
    DOI: 10.1080/03610926.2018.1489056
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    Cited by:

    1. Kaito Shimamura & Shuichi Kawano, 2021. "Bayesian sparse convex clustering via global-local shrinkage priors," Computational Statistics, Springer, vol. 36(4), pages 2671-2699, December.
    2. Banerjee, Sayantan, 2022. "Horseshoe shrinkage methods for Bayesian fusion estimation," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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