21 citations · 31 across the 4 of their papers we have counts for
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stat.ME2022
Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score
Shunsuke Imai, Takuya Koriyama, Shouto Yonekura +2
We introduce a new deal of kernel density estimation using an exponentiated form of kernel density estimators. The density estimator has two hyperparameters flexibly controlling th…
stat.ME2021★ 21 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.ME2021★ 1 cited
Adaptation of the Tuning Parameter in General Bayesian Inference with Robust Divergence
Shouto Yonekura, Shonosuke Sugasawa
We introduce a methodology for robust Bayesian estimation with robust divergence (e.g., density power divergence or γ-divergence), indexed by a single tuning parameter. It is well…