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Published and accsepted Papers (Peer-reviewed)

1. K. Nakamura, T. Nakagawa and K. Tahata (2024)
   Symmetry of Square Contingency Tables Using Simplicial Geometry.
   Austrian Journal of Statistics, Vol.53, No.4, 85-98.
   (DOI:https://doi.org/10.17713/ajs.v53i4.1845)
2. W. Urasaki, Y. Wada, T. Nakagawa, K. Tahata and S. Tomizawa (2024)
   Geometric Mean Type of Proportional Reduction in Variation Measure for Two-Way Contingency Tables.
   Sankhya B, 86, No.1, 139-163.
   (DOI:https://doi.org/10.1007/s13571-023-00320-w)
3. T. Momozaki, K. Cho, T. Nakagawa and S. Tomizawa (2024)
   Improving the accuracy of estimating indexes in contingency tables using Bayesian estimators.
   Journal of Statistical Theory and Practice, 18, Article number: 1.
   (DOI:https://doi.org/10.1007/s42519-023-00353-4)
4. S. Kimura, K. Ohata, H. Iihara, S. Nishioka,
   R. Ozeki, M. Saito, T. Nakagawa, M. Komoda (2023)
   Study on the development and implementation of electronic patient reported outcome-pharmaceutical management system for enhanced CINV control.
   Japanese Journal of Drug Informatics, Vol.25, No.3, 131–142. (in Japanese)
   (DOI: https://doi.org/10.11256/jjdi.25.131)
5. W. Urasaki, T. Nakagawa, T. Momozaki and S. Tomizawa (2023)
   Generalized Cramér's coefficient via f-divergence for contingency tables.
   Advances in Data Analysis and Classification, Published Online.
   (DOI: https://doi.org/10.1007/s11634-023-00560-8)
6. M. Hyodo, H. Watanabe, S. Nakagawa and T. Nakagawa (2023)
   Normalizing transformation of Dempster type statistic in high-dimensional settings.
   Communications in Statistics - Theory and Methods, Vol. 52, No.22, 8096–8113.
   (DOI:https://doi.org/10.1080/03610926.2022.2056749)
7. S. Sugasawa, T. Nakagawa, H. K. Solvang, S. Subbey and S. Alrabeei (2023)
   Dynamic Spatio-temporal Zero-inflated Poisson Models for Predicting Capelin Distribution in the Barents Sea.
   Japanese Journal of Statistics and Data Science, Vol.6, 1-20.
   (DOI:https://doi.org/10.1007/s42081-022-00183-x)
8. T. Momozaki, Y. Wada, T. Nakagawa and S. Tomizawa (2023)
   Extension of Generalized Proportional Reduction in Variation Measure for Two-Way Contingency Tables.
   Behaviormetrika, Vol.50, No.1, 385-398.
   (DOI:https://doi.org/10.1007/s41237-022-00186-8)
9. T. Momozaki, T. Nakagawa, K. Iki and S. Tomizawa (2023)
   An Index for the Degree and Directionality of Asymmetry for Square Contingency Tables with Ordered Categories.
   Austrian Journal of Statistics, Vol.52, No.1, 62-71.
   (DOI:https://doi.org/10.17713/ajs.v52i1.1382)
10. Y. Saigusa, N. Fukumoto, T. Nakagawa, and S. Tomizawa(2022)
    Measure of departure from conditional partial symmetry for square contingency tables.
    Journal of Mathematics and Statistics, Vol.18, No.1, 138-142.
    (DOI:https://doi.org/10.3844/jmssp.2022.138.142)
11. T. Nakagawa and S. Ohtsuka (2022)
    An asymptotic expansion for the distribution of Euclidean distance-based discriminant function in Normal populations.
    Journal of Statistical Theory and Practice, 16, Article number: 62. (DOI:https://doi.org/10.1007/s42519-022-00292-6)
12. K. Saito, N. Takakubo, A. Ishii, T. Nakagawa and S. Tomizawa (2022)
    Measures of Departure from Local Marginal Homogeneity for Square Contingency Tables.
    Symmetry, Vol. 14(6), 1075.
    (DOI:https://doi.org/10.3390/sym14061075)
13. 田中 蘭, 綿川 日菜, 中川 智之, 小林 正弘, 田畑 耕治, 松澤 智史 (2022)
    記述式評価データを用いた推薦システムの試作,
    オペレーション・リサーチ, 66, 2, 64-72. URL (in Japanese)
14. T. Nakagawa, R. Namba, K. Iki and S. Tomizawa (2021)
    Improved approximate unbiased estimators of the measure of departure from partial symmetry for square contingency tables.
    SUT Journal of Mathematics, Vol. 57, No.2, 167-183. (DOI:https://doi.org/10.55937/sut/1641859470)
15. T. Momozaki, T. Nakagawa, A. Ishii, Y. Saigusa and S. Tomizawa (2021)
    Two-dimensional index of departure from the symmetry model for square contingency tables with nominal categories.
    Symmetry, Vol. 13(11), 2031.
    (DOI:https://doi.org/10.3390/sym13112031)
16. T. Nakagawa, H. Watanabe and M. Hyodo (2021)
    Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional setting.
    Journal of Multivariate Analysis, Vol. 184, 104756.
    (DOI:https://doi.org/10.1016/j.jmva.2021.104756)
17. T. Nakagawa and S. Hashimoto (2021)
    On default priors for robust Bayesian estimation with divergences.
    Entropy, Vol. 23(1), 29.
    (DOI:https://doi.org/10.3390/e23010029)
18. T. Nakagawa, T. Takei, A. Ishii and S. Tomizawa(2020)
    Geometric mean type measure of marginal homogeneity for square contingency tables with ordered categories.
    Journal of Mathematics and Statistics, Vol. 16, No.1, 170-175.
    (DOI:https://doi.org/10.3844/jmssp.2020.170.175)
19. Y. Saigusa, T. Takada, A. Ishii, T. Nakagawa and S. Tomizawa (2020)
    Measure of departure from cumulative local symmetry for square contingency tables having ordered categories.
    Biometrical Letters: Journal of the Polish Biometric Society 57, No.1, 23-35.
    (DOI: https://doi.org/10.2478/bile-2020-0003)
20. T. Nakagawa and S. Hashimoto (2020)
    Robust Bayesian inference via γ-divergence.
    Communications in Statistics - Theory and Methods, Vol. 49, No.2, 343-360.
    (DOI:https://doi.org/10.1080/03610926.2018.1543765)
21. Y. Saigusa, M. Takami, A. Ishii, T. Nakagawa and S. Tomizawa (2019)
    Measure for departure from cumulative partial symmetry for square contingency tables with ordered categories.
    Journal of Statistics: Advances in Theory and Applications 21, No.1, 53-70.
    (DOI:http://dx.doi.org/10.18642/jsata_7100122036)
22. T. Nakagawa, S. Subbey and H. K. Solvang (2019)
    Integrating Hawkes process- and Biomass Models to Capture Impulsive Population Dynamics.
    Dynamics of Continuous, Discrete and Impulsive Systems Series B : Applications & Algorithms 26, No.3, 153-170. URL
23. T. Nakagawa (2018)
    Estimating the probabilities of misclassification using CV when the dimension and the sample sizes are large.
    Hiroshima Mathematical Journal, Vol.48, No.3, 373-411.
    (DOI:https://doi.org/10.32917/hmj/1544238034)
24. T. Nakagawa and H. Wakaki (2017)
    Selection of the linear and quadratic discriminant functions when the difference between two covariance matrices is small.
    Journal of the Japan Statistical Society, Vol. 47, No.2, 145-165.
    (DOI:https://doi.org/10.14490/jjss.47.145)
25. T. Tonda, T. Nakagawa and H. Wakaki (2017)
    EPMC Estimation in Discriminant Analysis when the Dimension and Sample Sizes are Large.
    Hiroshima Mathematical Journal, Vol.47, No.1, 43-62.
    (DOI:https://doi.org/10.32917/hmj/1492048847)

Proceedings (Peer-reviewed)

1. 北島茂樹, 山中脩也, 長慎也, 今野貴之, 中川 智之 (2024)
   プログラミング学習環境における能動的推論の活用とその評価.
   2024PC カンファレンス論文集, 93-96. (in Japanese)
2. 熊澤 努, 地嵜 頌子, 中川 智之, 室井 浩明, 渡邉 卓也 (2022)
   深層学習における正則化へのドロップアウトデザインの適用.
   「ソフトウェア・シンポジウム2022 論文集」, 1-10. (in Japanese)

Book Review

1. 中川 智之
   T. W. Anderson: An Introduction to Multivariate Statistical Analysis. 3rd ed., Wiley Ser. Probab. Stat., 2003 年, xx+721 ページ.
   『数学』, 第 73 巻, 第 3 号, 2021 年 7 月 夏季号, 331–334. (in Japanese)

Preprints and Working Papers

1. W. Urasaki, G. Kawamitsu, T. Nakagawa and K. Tahata (2024)
   A measure of departure from symmetry via the Fisher-Rao distance for contingency tables. (arXiv)
2. W. Urasaki, T. Nakagawa, J. Tsuchida and K. Tahata (2024)
   Visualization for departures from symmetry with the power-divergence-type measure in two-way contingency tables. (arXiv)
3. S. Orihara, S. Sugasawa, T. Ohigashi, T. Nakagawa and M. Taguri (2024)
   Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models. (arXiv)
4. W. Urasaki, T. Nakagawa and K. Tahata (2024)
   尺度に基づく対称性からの逸脱度の可視化と 非対称モデルとの関係.
   RIMS kokyuroku, No. 2284, 29-38.
5. S. Orihara, T. Momozaki and T. Nakagawa (2024)
   General Bayesian Inference for Causal Effects using Covariate Balancing Procedure. (arXiv, R-code)
6. T. Momozaki, T. Nakagawa, S. Sugasawa and H. K. Solvang (2023)
   Semiparametric Copula Estimation for Spatially Correlated Multivariate Mixed Outcomes: Analyzing Visual Sightings of Fin Whales from Line Transect Survey. (arXiv, R-code)
7. T. Momozaki and T. Nakagawa (2023)
   Robustness of Bayesian ordinal response model against outliers via divergence approach. (arXiv, R-code)
8. T. Momozaki and T. Nakagawa (2023)
   Ordinal response model におけるロバストダイバージェンスを用いた推定.
   RIMS kokyuroku, No. 2254, 59-68.
9. T. Momozaki and T. Nakagawa (2022)
   Robustness against outliers in ordinal response model via divergence approach. (arXiv, R-code)
10. T. Nakagawa, T. Momozaki, K. Cho and S. Tomizawa (2022)
    Choice of the Dirichlet parameter to estimate measures for square contingency tables.
    RIMS kokyuroku, No. 2221, 20-29.
11. 深井亮登, 朝生駿, 伊藤和輝, 國枝晃洋, 深井宏剛, 植田陸玖,
    小林正弘, 中川智之, 田畑耕治, 松澤智史 (2020)
    機械学習を用いた打球予測.
    統計数理研究所共同レポート439, 5, 35-44.
12. 井戸潤之祐, 朝生駿, 深井亮登, 深井宏剛, 中川智之, 松澤智史, 田畑耕治(2020)
    フェンシングにおける得点の要因分析.
    統計数理研究所共同レポート439, 7, 167-172.
13. T. Nakagawa (2019)
    Objective prior for the robust Bayesian estimation (in Japanese).
    RIMS kokyuroku, No. 2133, 40-49.
14. 中川智之, 髙石和輝, 白井駿弥, 髙橋恭平, 齋藤匠, 佐藤駿介, 田畑耕治, 松澤智史 (2019)
    機械学習と統計手法を用いた野球における局面予測.
    統計数理研究所共同レポート423, 5, 55-64.
15. T. Nakagawa (2018)
    クロスバリデーションによる誤判別確率に対するバイアス補正法.
    RIMS kokyuroku, No.2091, 38-54.
16. T. Nakagawa and S. Hashimoto (2017)
    Comparison of two robust Bayesian estimations by using the divergence under heavy contamination. (in Japanese)
    RIMS kokyuroku, No.2047, 55-66.
17. N. Chanohara, T. Nakagawa and H. Wakaki (2017)
    Estimation of covariance matrix via shrinkage Cholesky factor.
    Hiroshima Statistical Research Group Technical Report, TR 17-03.