2 citations · 2 across the 3 of their papers we have counts for
4 papers
Multinomial probit model based on joint quantile regression
Masaaki Okabe, Koki Matsuoka, Jun Tsuchida +1
The multinomial probit model is a typical statistical model for multiple-choice data applied in many research areas. When we are interested in some quantiles of relative utilities…
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…
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…