6 citations · 8 across the 11 of their papers we have counts for
8 papers · 1 filter
Principal component-guided sparse reduced-rank regression
Kanji Goto, Shintaro Yuki, Kensuke Tanioka +1
Reduced-rank regression estimates regression coefficients by imposing a low-rank constraint on the matrix of regression coefficients, thereby accounting for correlations among resp…
Regularized Sparse Optimal Discriminant Clustering
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a new method based on sparse optimal discriminant clustering (SODC), incorporating a penalty term into the scoring matrix based on convex clustering. With the addition o…
Estimating heterogeneous treatment effects by W-MCM based on Robust reduced rank regression
Ryoma Hieda, Shintaro Yuki, Kensuke Tanioka +1
Recently, from the personalized medicine perspective, there has been an increased demand to identify subgroups of subjects for whom treatment is effective. Consequently, the estima…
Extension of W-method and A-learner for multiple binary outcomes
Shintaro Yuki, Kensuke Tanioka, Hiroshi Yadohisa
In this study, we compared two groups, in which subjects were assigned to either the treatment or the control group. In such trials, if the efficacy of the treatment cannot be demo…
Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects
Mayu Hiraishi, Ke Wan, Kensuke Tanioka +2
This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-lea…
Wilcoxon-type Multivariate Cluster Elastic Net
Mayu Hiraishi, Kensuke Tanioka, Hiroshi Yadohisa
We propose a method for high dimensional multivariate regression that is robust to random error distributions that are heavy-tailed or contain outliers, while preserving estimation…