1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ME2025
Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity…
stat.ME2023
Bayesian Fused Lasso Modeling for Binary Data
Yuko Kakikawa, Shuichi Kawano
L1-norm regularized logistic regression models are widely used for analyzing data with binary response. In those analyses, fusing regression coefficients is useful for detecting gr…
stat.ME2022★ 1 cited
Bayesian Fused Lasso Modeling via Horseshoe Prior
Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano
Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a…