廣瀬慧研究室 [Kei Hirose Laboratory]

研究業績

原著論文

原著論文(査読あり)

[1] Hirose K., Wada, K., Hori, M. and Taniguchi, R. 
Event Effects Estimation on Electricity Demand Forecasting
To appear in Energies.

[2] 廣瀬慧.
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.
計算機統計学 2020年.

[3] Hirose K. and Masuda H.
Robust relative error estimation.
Entropy201820(9), 632.

[4] Hirose, K. and Imada, M.
Sparse factor regression via penalized maximum likelihood estimation.
Statistical Papers59(2), 633–662, 2018.

[5] Dolinský, J., Hirose K. and Konishi, S.
Readouts for Echo-State Networks Built using Locally Regularized Orthogonal Forward Regression.
Journal of Applied Statistics45(4), 740-762, 2018.

[6] Hirose K., Fujisawa, H. and Sese, J.
Robust sparse Gaussian graphical modeling.
Journal of Multivariate Analysis161, 172-190, 2017.
open access

[7] Imada, M., Hirose, K., Yoshida, M., Sunyong, K., Toyozumi, N., Lopez, G. and Kano, Y.
An Interpersonal Sentiment Quantification method applied to Work Relationship Prediction.
NTT Technical Review 15(3), 2017.

[8] Yamamoto, M., Hirose, K., Nagata, H.
Graphical tool of sparse factor analysis.
Behaviormetrika44(1), 229-250, 2017.

[9] 廣瀬慧.
スパースモデリングとモデル選択.
電子情報通信学会誌, 99巻, 5号, 392-399項, 2016年.
PDFファイル

[10] Hirose, K., Kim, S., Kano, Y., Imada, M., Yoshida, M., and Matsuo, M.
Full information maximum likelihood estimation in factor analysis with a large number of missing values.
Journal of Statistical Computation and Simulation, 86(1), 91-104, 2016.

[11] Hirose, K., Ogura, Y. and Shimodaira, H.
Estimating Scale-Free Networks via the Exponentiation of Minimax Concave Penalty.
Journal of the Japanese Society of Computational Statistics28, 139-154, 2015.

[12] Hirose, K. and Yamamoto, M.
Sparse estimation via nonconcave penalized likelihood in a factor analysis model.
Statistics and Computing25(5), 863-875. 2015.

[13] Hirose, K. and Yamamoto, M.
Estimation of an oblique structure via penalized likelihood factor analysis.
Computational Statistics & Data Analysis79, 120-132. 2014.

[14] Hirose, K., Tateishi, S. and Konishi, S.
Tuning parameter selection in sparse regression modeling.
Computational Statistics & Data Analysis59, 28-40, 2013.

[15] Hirose, K., and Higuchi, T.
Creating facial animation of characters via MoCap data.
Journal of Applied Statistics39(12), 2583-2597, 2012.

[16] Hirose, K. and Konishi, S.
Variable selection via the weighted group lasso for factor analysis models.
The Canadian Journal of Statistics, 40(2), 345-361,2012.

[17] Hirose, K., Kawano, S., Konishi, S. and Ichikawa, M.
Bayesian information criterion and selection of the number of factors in factor analysis models.
Journal of Data Science, 9, 243-259, 2011.

[18] 川野秀一,廣瀬慧,立石正平,小西貞則.
回帰モデリングと L1型正則化法の最近の展開
日本統計学会誌.39巻,2号,211-242頁.2010年.

[19] Hirose, K., Kawano, S., Miike, D. and Konishi, S.
HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS.
Bulletin of Informatics and Cybernetics42, 54-70, 2010.

[20] Hirose, K., Kawano, S. and Konishi, S.
BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION.
Bulletin of Informatics and Cybernetics40, 75-87, 2008.

原著論文(査読なし)

[1] 廣瀬慧.
因子分析モデルにおける構造正則化.
京都大学 数理解析研究所 講究録 2133巻 1-10頁 2019年.

[2] Hirose, K.
Editorial: Recent advances in sparse statistical modeling.
Journal of the Japanese Society of Computational Statistics28, 51-52, 2015.

[3] 廣瀬慧.
Lassoタイプの正則化法に基づくスパース推定法を用いた超高次元データ解析.
京都大学 数理解析研究所 講究録 1908巻 57-77頁 2014年.

プレプリント

[1] Okinaga Y., Kyogoku D., Kondo S., Nagano J. A., and Hirose K.
Effects of underlying gene-regulation network structure on prediction accuracy in high-dimensional regression.
bioRxiv:2020.09.11.293456, 2020.

[2] Hirose K.
Interpretable modeling for short- and medium-term electricity load forecasting.
arXiv:2006.01002 (arXiv), 2020.

[3] Hirose K. and Terada Y.
Simple structure estimation via prenet penalization.
arXiv:1607.01145 (arXiv), 2016.

九州大学 伊都キャンパス ウエスト1号館 D棟
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