廣瀬慧研究室 [Kei Hirose Laboratory]

原著論文

原著論文(査読あり)

  1. Teramoto, K., and Hirose, K.
    Sparse multivariate regression with missing values and its application to the prediction of material properties.
    Numerical Methods in Engineering, 123(2), 530-546, 2022.
    https://doi.org/10.1002/nme.6867

  2. Hirose, K.
    Interpretable modeling for short- and medium-term electricity demand forecasting.
    Frontiers in Energy Research, open access, 2021.
    https://doi.org/10.3389/fenrg.2021.724780

  3. Okinaga, Y., Kyogoku, D., Kondo, S., Nagano, A., and Hirose, K. 
    Relationship between gene regulation network structure and prediction accuracy in high dimensional regression.
    Scientific Reports, 11, 11483, 2021.
    https://doi.org/10.1038/s41598-021-90791-6

  4. Hirose, K., Wada, K., Hori, M., and Taniguchi, R. 
    Event Effects Estimation on Electricity Demand Forecasting.
    Energies, 13(21), 5839, 1-20, 2020.
    https://doi.org/10.3390/en13215839

  5. 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, 32巻, 1号, 45–60頁, 2020年.
    https://doi.org/10.20551/jscswabun.32.1_45

  6. Hirose K., and Masuda, H.
    Robust relative error estimation.
    Entropy, 20(9), 632, 1-24, 2018.
    https://doi.org/10.3390/e20090632

  7. Hirose, K., and Imada, M.
    Sparse factor regression via penalized maximum likelihood estimation.
    Statistical Papers59(2), 633–662, 2018.
    https://doi.org/10.1007/s00362-016-0781-8

  8. 川野 秀一, 松井 秀俊, 廣瀬 慧.
    “スパース推定法による統計モデリング”.
    共立出版, 2018年3月.
    https://www.kyoritsu-pub.co.jp/bookdetail/9784320112575

  9. 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.
    https://doi.org/10.1080/02664763.2017.1305331

  10. Hirose, K., Fujisawa, H., and Sese, J.
    Robust sparse Gaussian graphical modeling.
    Journal of Multivariate Analysis161, 172-190, 2017.
    https://doi.org/10.1016/j.jmva.2017.07.012

  11. 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.
    https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201703ra1.html-

  12. Yamamoto, M., Hirose, K., and Nagata, H.
    Graphical tool of sparse factor analysis.
    Behaviormetrika, 44(1), 229–250, 2017.
    https://doi.org/10.1007/s41237-016-0007-3

  13. 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頁, 2016年5月.
    http://www.keihirose.com/material/392-399_hirose.pdf

  14. 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.
    https://doi.org/10.1080/00949655.2014.995656

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6月.
    http://hdl.handle.net/2433/254796

プレプリント

  1. Hirose, K., Miura, K., and Koie, A.
    Hierarchical clustered multiclass discriminant analysis via crossvalidation.
    arXiv:2107.02324 (arXiv), 2021.
    https://arxiv.org/abs/2107.02324

  2. Hirose, K.
    Interpretable modeling for short- and medium-term electricity load forecasting.
    arXiv:2006.01002 (arXiv), 2020.
    https://arxiv.org/abs/2006.01002

  3. Hirose, K., and Terada, Y.
    Simple structure estimation via prenet penalization.
    arXiv:1607.01145 (arXiv), 2016.
    https://arxiv.org/abs/1607.01145

九州大学 伊都キャンパス ウエスト1号館 D棟
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