原著論文

(1) 原著論文(査読あり)

  1. Hirose, K., and Terada, Y.
    Sparse and simple structure estimation via prenet penalization.
    Psychometrika (in press).
  2. 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
  3. Hirose, K.
    Interpretable modeling for short- and medium-term electricity demand forecasting.
    Frontiers in Energy Research, 9, 2021.
    https://doi.org/10.3389/fenrg.2021.724780
  4. 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
  5. 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
  6. 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
  7. Hirose K., and Masuda, H.
    Robust relative error estimation.
    Entropy, 20(9), 632, 1-24, 2018.
    https://doi.org/10.3390/e20090632
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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-
  13. 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
  14. 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
  15. 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
  16. 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 Statistics, 28, 139–154, 2015.
    https://doi.org/10.5183/jjscs.1503001_215
  17. Hirose, K., and Yamamoto, M.
    Sparse estimation via nonconcave penalized likelihood in a factor analysis model.
    Statistics and Computing, 25(5), 863–875. 2015.
    https://doi.org/10.1007/s11222-014-9458-0
  18. Hirose, K., and Yamamoto, M.
    Estimation of an oblique structure via penalized likelihood factor analysis.
    Computational Statistics & Data Analysis, 79, 120–132. 2014.
    https://doi.org/10.1016/j.csda.2014.05.011
  19. Hirose, K., Tateishi, S., and Konishi, S.
    Tuning parameter selection in sparse regression modeling.
    Computational Statistics & Data Analysis, 59, 28–40, 2013.
    https://doi.org/10.1016/j.csda.2012.10.005
  20. Hirose, K., and Higuchi, T.
    Creating facial animation of characters via MoCap data.
    Journal of Applied Statistics, 39(12), 2583–2597, 2012.
    https://doi.org/10.1080/02664763.2012.724391
  21. 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.
    https://doi.org/10.1002/cjs.11129
  22. 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.
    http://www.jds-online.com/files/JDS-682.pdf
  23. 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.
    https://www.terrapub.co.jp/journals/jjssj/abstract/3902/39020211.html
  24. Hirose, K., Kawano, S., Miike, D., and Konishi, S.
    HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS.
    Bulletin of Informatics and Cybernetics, 42, 54–70, 2010.
    https://doi.org/10.5109/25906
  25. Hirose, K., Kawano, S., and Konishi, S.
    BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION.
    Bulletin of Informatics and Cybernetics, 40, 75–87, 2008.
    https://doi.org/10.5109/18995

(2) 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
  2. Hirose, K.
    Editorial: Recent advances in sparse statistical modeling.
    Journal of the Japanese Society of Computational Statistics28, 51–52, 2015.
    https://doi.org/10.5183/jjscs.1510002_225
  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, 5–77頁, 2014年.
    http://hdl.handle.net/2433/223166

(3) 査読付き国際会議論文

  1. Hirose, K., and Higuchi, T.
    Generating Artistic Character Facial Animation based on Motion Capture Data.
    Proceedings of The 2012 International Workshop on Advanced Image Technology, pp. 240–245, 2012.
  2. Hirose, H., Soejima, Y., and Hirose, K.
    NNRMLR: A Combined Method of Nearest Neighbor Regression and Multiple Linear Regression.
    Proceedings of 6th International Workshop on e-Activity, pp.351–356, 2012.
    https://doi.org/10.1109/IIAI-AAI.2012.76

(4) 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