5 papers
Groupwise Predictor Envelope Models for Multivariate Linear Regression
Sota Osumi, Akira Okazaki, Shuichi Kawano
Envelope methods improve estimation efficiency in multivariate analysis by isolating low-dimensional structures that contain all the information material to the parameter of intere…
Mixed-effects Outcome-Adaptive Lasso for Propensity Score Estimation under Partial Interference
Satoshi Nakashima, Akira Okazaki, Shuichi Kawano
Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided i…
Variable Fusion and Selection via a Spike-and-Slab Approach with Nonlocal Priors
Junya Miyake, Akira Okazaki, Shuichi Kawano
Variable fusion in linear regression models is a statistical method that identifies covariates making similar contributions to the response variable and imposes the same coefficien…
Multi-task learning via robust regularized clustering with non-convex group penalties
Akira Okazaki, Shuichi Kawano
Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are c…
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…