Kei Hirose

ランダムでない欠測を含む時系列モデリング

馬場由羽貴, 廣瀬慧

日本統計学会誌, 53(2) 275-296, Feb, 2024 Peer-reviewedLast author

Fast same-step forecast in SUTSE model and its theoretical properties

Wataru Yoshida, Kei Hirose

Computational Statistics and Data Analysis, 190, Feb, 2024

Sparse and Simple Structure Estimation via Prenet Penalization

Kei Hirose, Yoshikazu Terada

Psychometrika, 88(4) 1381-1406, Dec, 2023

Hierarchical clustered multiclass discriminant analysis via cross-validation

Kei Hirose, Kanta Miura, Atori Koie

Computational Statistics and Data Analysis, 178, Feb, 2023

Sparse multivariate regression with missing values and its application to the prediction of material properties

Keisuke Teramoto, Kei Hirose

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 123(2) 530-546, Jan, 2022

Interpretable Modeling for Short- and Medium-Term Electricity Demand Forecasting

Kei Hirose

FRONTIERS IN ENERGY RESEARCH, 9, Dec, 2021

Relationship between gene regulation network structure and prediction accuracy in high dimensional regression

Yuichi Okinaga, Daisuke Kyogoku, Satoshi Kondo, Atsushi J. Nagano, Kei Hirose

SCIENTIFIC REPORTS, 11(1), Jun, 2021

Event Effects Estimation on Electricity Demand Forecasting

Kei Hirose, Keigo Wada, Maiya Hori, Rin-ichiro Taniguchi

ENERGIES, 13(21), Nov, 2020

L1正則化法に基づく因子分析および構造方程式モデリングの最近の展開

廣瀬慧

計算機統計学会, Jun, 2020 Peer-reviewedInvited

Robust relative error estimation

Kei Hirose, Hiroki Masuda

Entropy, 20(9) 632-632, Aug, 2018 Peer-reviewed

Sparse factor regression via penalized maximum likelihood estimation.

Hirose, K. and Imada, M.

Statistical Papers, 633-662, May, 2018 Peer-reviewed

Readouts for echo-state networks built using locally regularized orthogonal forward regression

Ján Dolinský, Kei Hirose, Sadanori Konishi

Journal of Applied Statistics, 45(4) 740-762, Mar, 2018 Peer-reviewed

Robust sparse Gaussian graphical modeling

Kei Hirose, Hironori Fujisawa, Jun Sese

Journal of Multivariate Analysis, 161 172-190, Sep, 2017 Peer-reviewed

An Interpersonal Sentiment Quantification method applied to Work Relationship Prediction

Imada, M., Hirose, K., Yoshida, M., Sunyong, K., Toyozumi, N. …

NTT Technical Review, 15(3), Mar, 2017 Peer-reviewed

Graphical tool of sparse factor analysis

Yamamoto, M., Hirose, K., Nagata, H.

Behaviormetrika, Jan, 2017 Peer-reviewedInvited

Sparse modeling and model selection

Kei Hirose

Journal of the Institute of Electronics, Information and Communication Engineers, 99(5) 392-399, May, 2016 Peer-reviewed

Full information maximum likelihood estimation in factor analysis with a large number of missing values

Kei Hirose, Sunyong Kim, Yutaka Kano, Miyuki Imada, Manabu Yoshida, Masato Matsuo

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 86(1) 91-104, Jan, 2016

Sparse estimation via nonconcave penalized likelihood in factor analysis model

Kei Hirose, Michio Yamamoto

STATISTICS AND COMPUTING, 25(5) 863-875, Sep, 2015

ESTIMATING SCALE-FREE NETWORKS VIA THE EXPONENTIATION OF MINIMAX CONCAVE PENALTY

Kei Hirose, Yukihiro Ogura, Hidetoshi Shimodaira

JOURNAL JAPANESE SOCIETY OF COMPUTATIONAL STATISTICS, 28(1) 139-154, 2015

Estimation of an oblique structure via penalized likelihood factor analysis

Kei Hirose, Michio Yamamoto

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 79 120-132, Nov, 2014

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