原著論文

(1) 原著論文

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

[2]     Hirose K. and Masuda H.
Robust relative error estimation.
Entropy, 2018, 20(9), 632.

[3]   Hirose, K. and Imada, M.
Sparse factor regression via penalized maximum likelihood estimation.
Statistical Papers, 59(2), 633–662, 2018.

[4]     Dolinský, J., Hirose K. and Konishi, S.
Readouts for Echo-State Networks Built using Locally Regularized Orthogonal Forward Regression.
Journal of Applied Statistics, 45(4), 740-762, 2018.

[4]     Hirose K., Fujisawa, H. and Sese, J.
Robust sparse Gaussian graphical modeling.
Journal of Multivariate Analysis, 161, 172-190, 2017.
open access

[5]     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.

[6]     Yamamoto, M., Hirose, K., Nagata, H.
Graphical tool of sparse factor analysis.
Behaviormetrika, 44(1), 229-250, 2017.

[7]     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

[8]     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.

[10]     Hirose, K.
Editorial: Recent advances in sparse statistical modeling.
Journal of the Japanese Society of Computational Statistics, 28, 51-52, 2015.

[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 Statistics, 28, 139-154, 2015.

[12]   Hirose, K. and Yamamoto, M.
Sparse estimation via nonconcave penalized likelihood in a factor analysis model.
Statistics and Computing, 25(5), 863-875. 2015.

[13]   Hirose, K. and Yamamoto, M.
Estimation of an oblique structure via penalized likelihood factor analysis.
Computational Statistics & Data Analysis, 79, 120-132. 2014.

[14]   Hirose, K., Tateishi, S. and Konishi, S.
Tuning parameter selection in sparse regression modeling.
Computational Statistics & Data Analysis, 59, 28-40, 2013.

[15]   Hirose, K., and Higuchi, T.
Creating facial animation of characters via MoCap data.
Journal of Applied Statistics, 39(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 Cybernetics, 42, 54-70, 2010.

[20]   Hirose, K., Kawano, S. and Konishi, S.
BAYESIAN FACTOR ANALYSIS AND INFORMATION CRITERION.
Bulletin of Informatics and Cybernetics, 40, 75-87, 2008.

(2)  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.
Simple structure estimation via prenet penalization.
arXiv:1607.01145 (arXiv), 2020.

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