2 citations · 4 across the 5 of their papers we have counts for
5 papers
Empirical Bayes Method for Large-Scale Multiple Testing with Heteroscedastic Errors
Kwangok Seo, Johan Lim, Kaiwen Wang +3
In this paper, we address the normal mean inference problem, which involves testing multiple means of normal random variables with heteroscedastic variances. Most existing empirica…
Robust Global Fr'echet Regression via Weight Regularization
Hao Li, Shonosuke Sugasawa, Shota Katayama
The Fréchet regression is a useful method for modeling random objects in a general metric space given Euclidean covariates. However, the conventional approach could be sensitive to…
Adaptively Robust and Sparse K-means Clustering
Hao Li, Shonosuke Sugasawa, Shota Katayama
While K-means is known to be a standard clustering algorithm, its performance may be compromised due to the presence of outliers and high-dimensional noisy variables. This paper pr…
Robust and sparse Gaussian graphical modeling under cell-wise contamination
Shota Katayama, Hironori Fujisawa, Mathias Drton
Graphical modeling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this t…
Sparse and Robust Linear Regression: An Optimization Algorithm and Its Statistical Properties
Shota Katayama, Hironori Fujisawa
This paper studies sparse linear regression analysis with outliers in the responses. A parameter vector for modeling outliers is added to the standard linear regression model and t…