2 citations · 3 across the 10 of their papers we have counts for
11 papers
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…
Bayesian Geographically Weighted Regression using Fused Lasso Prior
Toshiki Sakai, Jun Tsuchida, Hiroshi Yadohisa
A main purpose of spatial data analysis is to predict the objective variable for the unobserved locations. Although Geographically Weighted Regression (GWR) is often used for this…
Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa
When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of co…
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…
Estimation and visualization of treatment effects for multiple outcomes
Shintaro Yuki, Kensuke Tanioka, Hiroshi Yadohisa
We consider a randomized controlled trial between two groups. The objective is to identify a population with characteristics such that the test therapy is more effective than the c…
F-measure Maximizing Logistic Regression
Masaaki Okabe, Jun Tsuchida, Hiroshi Yadohisa
Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all…
Bi-clustering for time-varying relational count data analysis
Satoshi Goto, Mariko Takagishi, Hiroshi Yadohisa
Relational count data are often obtained from sources such as simultaneous purchase in online shops and social networking service information. Bi-clustering such relational count d…