KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

Career 【 Display / hide 】

• 2013.04

  -

  2014.03

  Osaka University, Graduate School of Engineering Science, Research Fellowship for Young Scientists (PD)

• 2014.04

  -

  2014.05

  Transdisciplinary Research Integration Center, Research Organization of Information and Systems, Researcher

• 2014.06

  -

  2019.03

  Tokyo Institute of Technology, School of Engineering, Department of Industrial Engineering and Economics, Assistant Professor

• 2019.04

  -

  Present

  Keio University, Faculty of Economics, Associate Professor

Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 2005.04

  -

  2009.03

  Doshisha University, Culture and Information Science

  University, Graduated

• 2009.04

  -

  2011.03

  Osaka University, Graduate School of Engineering Science, システム創成専攻

  Graduate School, Completed, Master's course

• 2011.04

  -

  2013.03

  Osaka University, Graduate School of Engineering Science, システム創成専攻

  Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• Ph.D. in Science, Osaka University, Coursework, 2013.03

  Statistical Inference Concerning a High-Dimensional Mean Vector

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Informatics / Statistical science

Research Keywords 【 Display / hide 】

Research Keywords 【 Display / hide 】

• Graphical modeling

• Sparse Estimation

• Model selection

• Robust inference

• High dimensional data

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Adaptively Robust and Sparse K-means Clustering

  Li H., Sugasawa S., Katayama S.

  Transactions on Machine Learning Research 2024 2024

  　View Summary

  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 proposes adaptively robust and sparse K-means clustering (ARSK) to address these practical limitations of the standard K-means algorithm. For robustness, we introduce a redundant error component for each observation, and this additional parameter is penalized using a group sparse penalty. To accommodate the impact of high-dimensional noisy variables, the objective function is modified by incorporating weights and implementing a penalty to control the sparsity of the weight vector. The tuning parameters to control the robustness and sparsity are selected by Gap statistics. Through simulation experiments and real data analysis, we demonstrate the proposed method’s superiority to existing algorithms in identifying clusters without outliers and informative variables simultaneously.

• Positive-definite modification of a covariance matrix by minimizing the matrix ℓ<inf>∞</inf> norm with applications to portfolio optimization

  Cho S., Katayama S., Lim J., Choi Y.G.

  AStA Advances in Statistical Analysis (AStA Advances in Statistical Analysis)  105 ( 4 ) 601 - 627 2021

  Research paper (scientific journal), Joint Work, Accepted,  ISSN  18638171

  　View Summary

  © 2021, Springer-Verlag GmbH Germany, part of Springer Nature. The covariance matrix, which should be estimated from the data, plays an important role in many multivariate procedures, and its positive definiteness (PDness) is essential for the validity of the procedures. Recently, many regularized estimators have been proposed and shown to be consistent in estimating the true matrix and its support under various structural assumptions on the true covariance matrix. However, they are often not PD. In this paper, we propose a simple modification to make a regularized covariance matrix be PD while preserving its support and the convergence rate. We focus on the matrix ℓ∞ norm error in covariance matrix estimation because it could allow us to bound the error in the downstream multivariate procedure relying on it. Our proposal in this paper is an extension of the fixed support positive-definite (FSPD) modification by Choi et al. (2019) from spectral and Frobenius norms to the matrix ℓ∞ norm. Like the original FSPD, we consider a convex combination between the initial estimator (the regularized covariance matrix without PDness) and a given form of the diagonal matrix minimize the ℓ∞ distance between the initial estimator and the convex combination, and find a closed-form expression for the modification. We apply the procedure to the minimum variance portfolio (MVP) optimization problem and show that the vector ℓ∞ error in the estimation of the optimal portfolio weight is bounded by the matrix ℓ∞ error of the plug-in covariance matrix estimator. We illustrate the MVP results with S&P 500 daily returns data from January 1978 to December 2014.

  • Access to Document (DOI)

• Computational and Statistical Analyses for Robust Non-convex Sparse Regularized Regression Problem

  Shota Katayama

  Journal of Statistical Planning and Inference (Elsevier)  201   20 - 31 2019

  Research paper (scientific journal), Single Work, Accepted

• Robust and sparse Gaussian graphical modeling under cell-wise contamination

  Shota Katayama, Hironori Fujisawa, Mathias Drton

  Stat (Wiley)  7 ( 1 ) e181 2018

  Research paper (scientific journal), Joint Work, Accepted

• Sparse and Robust Linear Regression: An Optimization Algorithm and Its Statistical Properties

  Shota Katayama, Hironori Fujisawa

  Statistica Sinica 27   1243 - 1264 2017

  Research paper (scientific journal), Joint Work, Accepted

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• Development of discovering statistical methods via sparse modeling

  Katayama, Shota

  科学研究費補助金研究成果報告書 2023

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Research Projects of Competitive Funds, etc. 【 Display / hide 】

• スパースモデリングによる発見的統計手法の開発

  2018.04

  -

  2022.03

  日本学術振興会, 若手研究, No Setting, Principal investigator

• スパース正則化法による複雑高次元データ解析法の確立

  2015.04

  -

  2018.03

  日本学術振興会, 若手研究(B), No Setting, Principal investigator

• 高次元データに対する統計的推測の研究

  2011.04

  -

  2013.03

  日本学術振興会, 特別研究員奨励費, No Setting, Principal investigator

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• INDEPENDENT RESEARCH PROJECT C

  2026

• RESEARCH SEMINAR D

  2026

• STATISTICS 2

  2026

• DATA SCIENCE AND ARTIFICIAL INTELLIGENCE

  2026

• STATISTICS 1

  2026

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