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20182021
most citedOn Selection Criteria for the Tuning Parameter in Robust Divergence

21 citations · 25 across the 6 of their papers we have counts for

collaborators

18 papers

stat.ME202121 cited

On Selection Criteria for the Tuning Parameter in Robust Divergence

Shonosuke Sugasawa, Shouto Yonekura

While robust divergence such as density power divergence and -divergence is helpful for robust statistical inference in the presence of outliers, the tuning parameter that contr…

stat.ME20211 cited

Adaptively Robust Geographically Weighted Regression

Shonosuke Sugasawa, Daisuke Murakami

We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function…

stat.ME2021

General Unbiased Estimating Equations for Variance Components in Linear Mixed Models

Tatsuya Kubokawa, Shonosuke Sugasawa, Hiromasa Tamae +1

This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used esti…

stat.ME2020

Spatially Clustered Regression

Shonosuke Sugasawa, Daisuke Murakami

Spatial regression or geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a r…

stat.ME20201 cited

Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model

Takumi Saegusa, Shonosuke Sugasawa, Partha Lahiri

The multivariate Fay-Herriot model is quite effective in combining information through correlations among small area survey estimates of related variables or historical survey esti…

stat.ME2020

Log-Regularly Varying Scale Mixture of Normals for Robust Regression

Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa

Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential…