7 papers
Covariate-dependent Nonparametric -modeling for regression via infinite Mixture-of-Expertizing class
Akira Okazaki, Keisuke Yano
Empirical Bayes -modeling captures unit-level heterogeneity by estimating a latent prior distribution from observed data. In the existing formulations, however, the prior is sha…
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…
Multi-Task Learning Regression via Convex Clustering
Akira Okazaki, Shuichi Kawano
Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL,…
Multi-task Learning for Compositional Data via Sparse Network Lasso
Akira Okazaki, Shuichi Kawano
A network lasso enables us to construct a model for each sample, which is known as multi-task learning. Existing methods for multi-task learning cannot be applied to compositional…