{"id":1005,"date":"2021-12-27T13:56:33","date_gmt":"2021-12-27T04:56:33","guid":{"rendered":"https:\/\/www.shimizu.sci.waseda.ac.jp\/wordpress\/?page_id=1005"},"modified":"2025-10-31T14:26:38","modified_gmt":"2025-10-31T05:26:38","slug":"papers","status":"publish","type":"page","link":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/research\/papers\/","title":{"rendered":"Papers"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u7814\u7a76\u5ba4\u30e1\u30f3\u30d0\u30fc\u306b\u3088\u308b\u51fa\u7248\u8ad6\u6587<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ning, B. and Shimizu, Y. (2025). From CKLS process to CIR-type and OU-type processes: Using a twice-differentiable mapping and generalized Girsanov\u2019s theorem, 2025,\u00a0<em>accepted in<\/em>\u00a0<a href=\"https:\/\/link.springer.com\/journal\/10690\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>Asia-Pacific Financial Markets<\/em><\/strong><\/a>. (<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10690-025-09563-1\" target=\"_blank\" rel=\"noreferrer noopener\">DOI<\/a>)<\/li>\n\n\n\n<li>Irie, H. and Shimizu, Y. (2024). Approximation and estimation of scale functions for spectrally negative Levy processes,&nbsp;to appear in<em> Journal of Applied Probability;&nbsp;<\/em><strong><em><a href=\"https:\/\/arxiv.org\/abs\/2402.13599\" target=\"_blank\" rel=\"noreferrer noopener\">arXiv:2402.13599<\/a><\/em><\/strong><\/li>\n\n\n\n<li>Mitsuta, D. and Shimizu, Y. (2024). Mortality Prediction using Survival Energy Models with Functional Data Analysis, <em><a href=\"https:\/\/www.springer.com\/journal\/42081\"><strong>Japanese Journal of Statistics and Data Science<\/strong><\/a><\/em>, <strong>7<\/strong>, (2), 841-859:<em>&nbsp;A special issue \u201cRisk and Statistics in Actuarial Science\u201d<\/em>:&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2402.06138\" target=\"_blank\" rel=\"noreferrer noopener\"><em><strong>arXiv:2402.06138<\/strong><\/em><\/a>. (<a href=\"https:\/\/doi.org\/10.1007\/s42081-024-00245-2\" target=\"_blank\" rel=\"noreferrer noopener\">DOI<\/a>)<\/li>\n\n\n\n<li>Takagami, Y. and Shimizu, Y. (2024). Prediction of information leakage by cyber incidents via classical compound risk models,&nbsp;<em><a href=\"https:\/\/www.springer.com\/journal\/42081\"><strong>Japanese Journal of Statistics and Data Science<\/strong><\/a>, <\/em><strong>7<\/strong>, (2), 1059-1084: <em>a special issue \u201cRisk and Statistics in Actuarial Science\u201d;&nbsp;<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4450319\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>SSRN:4450319<\/strong><\/a><\/em>. (<a href=\"https:\/\/doi.org\/10.1007\/s42081-024-00273-y\" target=\"_blank\" rel=\"noreferrer noopener\">DOI<\/a>)<\/li>\n\n\n\n<li>Shirai, K. and Shimizu, Y. (2023). Calculation of prediction intervals for complete life expectancy by the survival energy models, <em>to appear in <\/em><a href=\"https:\/\/www.jarip.org\/publication\/jarip_jounal\/index.html\" data-type=\"link\" data-id=\"https:\/\/www.jarip.org\/publication\/jarip_jounal\/index.html\"> <strong><em><strong><em>J. Japanese Association of Risk, Insurance and Pensions<\/em><\/strong><\/em><\/strong><\/a>.<\/li>\n\n\n\n<li>Shimizu, Y; Shirai, K.; Kojima, Y.; Mitsuda, D. and Inoue, M. (2023). Survival energy models for mortality prediction and the future prospects, 2023,&nbsp;<strong><strong><a href=\"https:\/\/www.cambridge.org\/core\/journals\/astin-bulletin-journal-of-the-iaa#\" target=\"_blank\" rel=\"noreferrer noopener\">ASTIN Bulletin<\/a><\/strong><\/strong>, 53, (2), 377-391. (<a href=\"https:\/\/doi.org\/10.1017\/asb.2023.10\" target=\"_blank\" rel=\"noreferrer noopener\">DOI)<\/a><em>.<\/em><\/li>\n\n\n\n<li>Huo, W. and Shimizu, Y. (2023). A tail estimate for empirical processes of multivariate Gaussian under general dependence,&nbsp;preprint,&nbsp;<em><a href=\"https:\/\/arxiv.org\/abs\/2303.11635\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>arXive:2303.11635<\/strong><\/a><\/em>.<\/li>\n\n\n\n<li>Nemoto, H. and Shimizu, Y. (2023). Statistical inference for discretely sampled stochastic functional differential equations with small noise,<em> <a href=\"http:\/\/www.springer.com\/math\/probability\/journal\/11203\"><em><strong>Statistical Inference and Stochastic Processes<\/strong><\/em><\/a><\/em>, <strong>27<\/strong>, (2), 427\u2013456;&nbsp;<strong><em><a href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/ys\/publications\/2303.10807\" target=\"_blank\" rel=\"noreferrer noopener\">arXiv:2303.10807<\/a><\/em><\/strong>.(<a href=\"https:\/\/doi.org\/10.1007\/s11203-023-09299-7\">DOI<\/a>)<\/li>\n\n\n\n<li>Kobayashi, M. and Shimizu, Y. (2023). Threshold estimation for jump-diffusions under small noise asymptotic,&nbsp;<a href=\"http:\/\/www.springer.com\/math\/probability\/journal\/11203\"><em><strong>Statistical Inference and Stochastic Processes<\/strong><\/em><\/a>,&nbsp;<strong>26<\/strong>, 361-411. (<a href=\"https:\/\/doi.org\/10.1007\/s11203-023-09286-y\" target=\"_blank\" rel=\"noreferrer noopener\">DOI<\/a>).<\/li>\n\n\n\n<li>Shimizu, Y.; Shirai, K.; Kojima, Y.; Mitsuda, D. and Inoue, M. (2022). A new survival energy model and SEM project; <strong><em><a href=\"http:\/\/ssrn.com\/abstract=4127900\">SSRN:4127900<\/a><\/em><\/strong>.  <\/li>\n\n\n\n<li>Nakajima, S. and Shimizu, Y. (2022). Parametric inference for stochastic differential equations driven by multiplicative fractional noise, <em>to appear in <strong><a rel=\"noreferrer noopener\" href=\"https:\/\/www.springer.com\/journal\/42081\" target=\"_blank\"><em>Japanese Journal of Statistics and Data Science<\/em><\/a><\/strong><\/em>.  <\/li>\n\n\n\n<li>Nakamura, S and Nakajima, S. and Shimizu, Y. (2022). Least-squares estimators for discretely observed stochastic processes driven by small fractional noise, <em>submitted<\/em>,&nbsp;<strong><em><a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/abs\/2201.08462\" target=\"_blank\">arXiv:2201.08462<\/a><\/em><\/strong>.<\/li>\n\n\n\n<li>Chen, C.; Sato, T.; Kawaguchi, S.; Ye, X and Shimizu, Y. (2021). Mortality RateForecasting Using Compositionally-Warped Gaussian Processes Equipped with Grid Search, preprint.<\/li>\n\n\n\n<li>Nakamura, S. and Shimizu, Y. (2023). Estimating the finite-time ruin probability of a surplus with a long memory via Malliavin calculus;&nbsp;<a href=\"https:\/\/arxiv.org\/abs\/2206.09441\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>arXiv:2206.09441<\/em><\/strong><\/a>,&nbsp;<em><em><strong>Research Papers in Statistical Inference for Time Series and Related Models<\/strong><\/em>,&nbsp;<\/em>455-474<em>.&nbsp;<\/em>(<a href=\"https:\/\/doi.org\/10.1007\/978-981-99-0803-5\" target=\"_blank\" rel=\"noreferrer noopener\">DOI<\/a>)<\/li>\n\n\n\n<li>Nakajima, S. and Shimizu, Y. (2022). Parameter estimation of stochastic differential equation driven by small fractional noise, <em><strong><a rel=\"noreferrer noopener\" href=\"https:\/\/www.tandfonline.com\/journals\/gsta20\" target=\"_blank\">Statistics<\/a><\/strong><\/em>, <strong>56<\/strong>, (4), 919-934. (<a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.1080\/02331888.2022.2098960\" target=\"_blank\">DOI<\/a>); <a href=\"https:\/\/arxiv.org\/abs\/2201.00372\"><strong><em>arXiv:2201.00372<\/em><\/strong><\/a>.<\/li>\n\n\n\n<li>Kobayashi, M. and Shimizu, Y. (2022).  Least-squares estimators based on the Adams method for stochastic differential equations with small L\u00e9vy noise, <em><a rel=\"noreferrer noopener\" style=\"font-weight: bold;\" href=\"https:\/\/www.springer.com\/journal\/42081\" target=\"_blank\">Japanese Journal of Statistics and Data Science<\/a><\/em>.<strong> 5<\/strong>, 217-240; (<a rel=\"noreferrer noopener\" href=\"https:\/\/link.springer.com\/article\/10.1007\/s42081-022-00155-1\" target=\"_blank\">DOI<\/a>); <strong><em><a rel=\"noreferrer noopener\" href=\"https:\/\/arxiv.org\/abs\/2201.06787\" target=\"_blank\">arXiv:2201.06787<\/a><\/em><\/strong>.<\/li>\n\n\n\n<li>Shimziu, Y. and Nakajima, S. (2021). Asymptotic normality of least squares estimators to stochastic differential equations driven by fractional Brownian motions, <em><a href=\"https:\/\/www.sciencedirect.com\/journal\/statistics-and-probability-letters\"><strong>Statistics and Probability Letters<\/strong><\/a>.<\/em> <strong>187<\/strong>, 109476 (<a rel=\"noreferrer noopener\" href=\"https:\/\/doi.org\/10.1016\/j.spl.2022.109476\" target=\"_blank\">DOI<\/a>)<\/li>\n\n\n\n<li>Minami, Y.; Ito, R. and Shimizu, Y. (2020). Why does a human die? A structural approach to cohort-wise mortality prediction under Survival Energy Hypothesis,&nbsp;<em><strong><a rel=\"noreferrer noopener\" href=\"https:\/\/www.cambridge.org\/core\/journals\/astin-bulletin-journal-of-the-iaa#\" target=\"_blank\">ASTIN Bulletin<\/a><\/strong><\/em>, <strong>51<\/strong>, (1) 191-219; (<a href=\"https:\/\/www.cambridge.org\/core\/journals\/astin-bulletin-journal-of-the-iaa\/article\/why-does-a-human-die-a-structural-approach-to-cohortwise-mortality-prediction-under-survival-energy-hypothesis\/68D35C4EB48E37E53EE0ED6403DB8460\">Open Access<\/a>).<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4f0a\u85e4\u9f8d\u4e4b\u4ecb\uff0c\u6e05\u6c34\u6cf0\u9686 (2019). \u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u4eee\u8aac\u306b\u57fa\u3065\u304f\u69cb\u9020\u30a2\u30d7\u30ed\u30fc\u30c1\u3068\u30b3\u30db\u30fc\u30c8\u5225\u6b7b\u4ea1\u7387\u63a8\u5b9a, <strong><em>\u65e5\u672c\u4fdd\u967a\u30fb\u5e74\u91d1\u30ea\u30b9\u30af\u5b66\u4f1a\u30fb\u5927\u4f1a\u30d7\u30ed\u30b7\u30fc\u30c7\u30a3\u30f3\u30b0\u30b9\u7279\u96c6\u53f7<\/em><\/strong>, 6, 17\u201330.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Oshime, T. and Shimizu, Y. (2018). Parametric inference for ruin probability in the classical risk model, <em><a href=\"https:\/\/www.sciencedirect.com\/journal\/statistics-and-probability-letters\"><strong>Statistics and Probability Letters<\/strong><\/a><\/em>, <strong>133<\/strong>, 28-37. (<a href=\"https:\/\/doi.org\/10.1016\/j.spl.2017.09.020\">DOI<\/a>)<\/li>\n\n\n\n<li>Nakahira, K.; Takahashi, K.; Kakegawa, K. and Shimizu, Y. (2017). Evauation of IBNR researves with the data of reported numbers (in Japanese), Proceedings of JARIP conference 2017: a special issue of <strong><em><strong><em>J. Japanese Association of Risk, Insurance and Pensions<\/em><\/strong><\/em><\/strong>, <strong>4<\/strong>, 147-158.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Works by Prof. Shimizu<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/ys\/publications\/\" target=\"_blank\" rel=\"noreferrer noopener\">To his own home page<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">\u5352\u696d\u30fb\u5b66\u4f4d\u8ad6\u6587\uff08\u5b66\u58eb\u30fb\u4fee\u58eb\u30fb\u535a\u58eb\uff09<\/h2>\n\n\n\n<h4 class=\"wp-block-heading is-style-vk-heading-background_fill_lightgray\">\u4fee\u58eb<\/h4>\n\n\n\n<p><strong><strong>2025\u5e74\u5ea6<\/strong>\uff089\u6708\u5352\u696d\uff09<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Two-Step Estimation of Food Loss Using Functional Regression and Functional Kriging Method\uff08Satoshi Kaifu\uff09<\/li>\n\n\n\n<li>Mortality prediction via functional data analysis with a transformation approach\uff08Sho Sakagami\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2024\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30a8\u30eb\u30b4\u30fc\u30c9\u6027\u3092\u3082\u3064\u5b9a\u5e38\u30ac\u30a6\u30b9\u904e\u7a0b\u306b\u5bfe\u3059\u308b\u5171\u5206\u6563\u95a2\u6570\u306e\u63a8\u5b9a\uff08\u65b0\u4e95\u5eb7\u592a\uff09<\/li>\n\n\n\n<li>Adaptive Bayes estimator for stochastic differential equations with jumps under small noise asymptotics\uff08\u9234\u6728\u4fca\u592a\u90ce\uff09<\/li>\n\n\n\n<li>Gerber-Shiu\u95a2\u6570\u3092\u7528\u3044\u305f\u6709\u9650\u6642\u9593\u7834\u7523\u78ba\u7387\u306e\u63a8\u5b9a\u624b\u6cd5\u306e\u63d0\u6848\uff08\u5e73\u672c\u7fd4\u4e5f\uff09<\/li>\n\n\n\n<li>Win-Lose Prediction in Team Sports and Player\u2019s Potential Assessment Beyond STATS\uff08\u5c71\u91ce\u908a\u6566\u53f2\uff09<\/li>\n\n\n\n<li>Analyzing Chinese Securities Market Risk Management Using GARCH Model\uff08Yuanda GUO\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2023\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u8ca0\u306e\u30b8\u30e3\u30f3\u30d7\u3092\u3082\u3064Le\u0301vy \u904e\u7a0b\u306e\u96e2\u6563\u89b3\u6e2c\u306b\u3088\u308bq-\u30b9\u30b1\u30fc\u30eb\u95a2\u6570\u306e\u63a8\u5b9a\u91cf\u306e\u69cb\u6210\u3068\u305d\u306e\u6f38\u8fd1\u7684\u6027\u8cea\u306b\u3064\u3044\u3066\uff08\u5165\u6c5f\u907c\uff09<\/li>\n\n\n\n<li>Hawkes\u904e\u7a0b\u306e\u7406\u8ad6\u3068\u5fdc\u7528\uff08\u5185\u91ce\u572d\u8f14\uff09<\/li>\n\n\n\n<li>ARIMA\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u30de\u30eb\u30c1\u30d7\u30eb\u63a8\u5b9a\u53ca\u3073\u30dd\u30fc\u30c8\u30d5\u30a9\u30ea\u30aa\u69cb\u6210\u624b\u6cd5\u306e\u63d0\u6848\uff08\u7247\u5c71\u7adc\u592a\u6717\uff09<\/li>\n\n\n\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\uff08SEM\uff09\u3092\u7528\u3044\u305f\u82e5\u5e74\u5c64\u306e\u6b7b\u4ea1\u7387\u4e88\u6e2c\uff08\u6749\u5c71\u9686\u4eba\uff09<\/li>\n\n\n\n<li>\u9023\u7d9a\u89b3\u6e2c\u306b\u304a\u3051\u308b\u30ac\u30a6\u30b9\u904e\u7a0b\u306e\u53b3\u5bc6\u306a\u5c24\u5ea6\u95a2\u6570\u306b\u3088\u308b\u6c4e\u95a2\u6570\u63a8\u5b9a\uff08\u9ad8\u5ca1\u4f38\u65ec\uff09<\/li>\n\n\n\n<li>\u30ac\u30a6\u30b9\u904e\u7a0b\u306e\u53b3\u5bc6\u306a\u5c24\u5ea6\u95a2\u6570\u3092\u7528\u3044\u305f\u5e73\u5747\u95a2\u6570\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u63a8\u5b9a\uff08\u897f\u8107\u512a\u6597\uff09<\/li>\n\n\n\n<li>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30ab\u30fc\u30cd\u30eb\u5bc6\u5ea6\u63a8\u5b9a\uff08\u98db\u7530\u7dbe\u4e5f\uff09<\/li>\n\n\n\n<li>ID-SEM\u3092\u7528\u3044\u305f\u95a2\u6570\u30c7\u30fc\u30bf\u89e3\u6790\u306b\u3088\u308b\u6b7b\u4ea1\u7387\u4e88\u6e2c\uff08\u5149\u7530\u5927\u8f1d\uff09<\/li>\n\n\n\n<li>\u30aa\u30fc\u30c0\u30fc\u30d6\u30c3\u30af\u306b\u3088\u308b\u76f8\u5834\u53c2\u52a0\u8005\u306e\u884c\u52d5\u306e\u30e2\u30c7\u30ea\u30f3\u30b0\u3068\u682a\u4fa1\u306e\u30b7\u30e5\u30df\u30ec\u30fc\u30b7\u30e7\u30f3\uff08\u5c71\u6839\u4f51\u592a\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2022\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5fae\u5c0f\u62e1\u6563\u904e\u7a0b\u306e\u30c9\u30ea\u30d5\u30c8\u4fc2\u6570\u306b\u5bfe\u3059\u308b\u96e2\u6563\u89b3\u6e2c\u306b\u3088\u308b\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u63a8\u5b9a\uff08\u6d45\u91ce\u4e00\u6d77\uff09<\/li>\n\n\n\n<li>IG-SEM\u3092\u7528\u3044\u305f\u6b7b\u4ea1\u7387\u4e88\u6e2c\u306e\u6539\u5584 -\u95a2\u6570\u30c7\u30fc\u30bf\u89e3\u6790\u3092\u7528\u3044\u305f\u95a2\u6570\u63a8\u5b9a-\uff08\u5c0f\u5cf6\u7950\u592a\uff09<\/li>\n\n\n\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u5b8c\u5168\u5e73\u5747\u4f59\u547d\u306e\u4e88\u6e2c\u533a\u9593\u7b97\u5b9a\uff08\u767d\u4e95\u4f3d\u5948\uff09<\/li>\n\n\n\n<li>\u30b5\u30a4\u30d0\u30fc\u30a2\u30bf\u30c3\u30af\u306b\u3088\u308b\u60c5\u5831\u6f0f\u6d29\u4e88\u6e2c\uff5e\u8907\u5408\u5206\u5e03\u3068\u30d0\u30ea\u30e5\u30fc\u30fb\u30a2\u30c3\u30c8\u30fb\u30ea\u30b9\u30af\uff5e\uff08\u9ad8\u4e0a\u96c4\u592a\u90ce\uff09<\/li>\n\n\n\n<li>\u95a2\u6570\u578b\u78ba\u7387\u5fae\u5206\u65b9\u7a0b\u5f0f\u53ca\u3073\u98db\u8e8d\u578b\u62e1\u6563\u904e\u7a0b\u306b\u5bfe\u3059\u308b\u96e2\u6563\u89b3\u6e2c\u306b\u57fa\u3065\u304f\u7d71\u8a08\u7684\u63a8\u6e2c\uff08\u6839\u672c\u6ec9\u6689\uff09<\/li>\n\n\n\n<li>\u30ac\u30a6\u30b9\u30ab\u30fc\u30cd\u30eb\u5bc6\u5ea6\u63a8\u5b9a\u3092\u7528\u3044\u305f\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u306e\u533a\u9593\u63a8\u5b9a\uff08\u539f\u7530\u62d3\u5b9f\uff09<\/li>\n\n\n\n<li>\u6642\u9593\u9045\u308c\u3092\u3082\u3064\u5fae\u5c0f\u62e1\u6563\u78ba\u7387\u7a4d\u5206\u65b9\u7a0b\u5f0f\u306b\u304a\u3051\u308b\u6700\u5c24\u63a8\u5b9a\u91cf\u306e\u6f38\u8fd1\u6b63\u898f\u6027\uff08\u5897\u7530\u5553\u592a\uff09<\/li>\n\n\n\n<li>A tail estimate for empirical processes of multivariate Gaussian under general dependence and its application\uff08Wen HUO\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2021\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u9023\u7d9a\u6642\u9593\u306b\u304a\u3051\u308b\u5909\u984d\u990a\u8001\u4fdd\u967a\u306e\u4fdd\u967a\u8ca0\u50b5\u8a55\u4fa1\u3068\u8003\u5bdf\uff08\u9060\u85e4\u5927\u5730\uff09<\/li>\n\n\n\n<li><span style=\"color: initial;\">\u7834\u7523\u78ba\u7387\u306e\u7d71\u8a08\u7684\u63a8\u6e2c\uff08\u91d1\u4e95\u52c7\u6a39\uff09<\/span><\/li>\n\n\n\n<li>\u30ac\u30a6\u30b9\u904e\u7a0b\u56de\u5e30\u3092\u7528\u3044\u305f\u4e8c\u9178\u5316\u70ad\u7d20\u6fc3\u5ea6\u306e\u4e88\u6e2c\uff08\u6cb3\u53e3\u771f\uff09<\/li>\n\n\n\n<li>Le\u0301vy \u904e\u7a0b\u3092\u7528\u3044\u305f\u7d2f\u7a4d\u6b7b\u4ea1\u7387\u30e2\u30c7\u30ea\u30f3\u30b0\uff08\u4f50\u4e95\u7ae0\u4eba\uff09<\/li>\n\n\n\n<li>CWGP \u3092\u7528\u3044\u305f\u591a\u6b21\u5143\u30ac\u30a6\u30b9\u904e\u7a0b\u56de\u5e30\u306b\u3088\u308b\u6b7b\u4ea1 \u7387\u4e88\u6e2c\uff08\u4f50\u85e4\u7ffc\uff09<\/li>\n\n\n\n<li>\u975e\u6574\u6570\u30d6\u30e9\u30a6\u30f3\u904b\u52d5\u3067\u99c6\u52d5\u3055\u308c\u308b\u78ba\u7387\u5fae\u5206\u65b9\u7a0b\u5f0f\u306e\u30c9\u30ea\u30d5\u30c8\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a8\u5b9a\u6cd5\uff0c\u53ca\u3073\u305d\u306e\u6c4e\u95a2\u6570\u306e\u671f\u5f85\u5024\u306b\u95a2\u3059\u308b\u30d7\u30e9\u30b0\u30a4\u30f3\u63a8\u5b9a\u91cf\u306e\u6f38\u8fd1\u7684\u6027\u8cea\uff08\u4e2d\u6751\u54b2\u592a\uff09<\/li>\n\n\n\n<li>A comparative study of different Gaussian process regressions predicting financial data (Xiaomin Ye)<\/li>\n<\/ul>\n\n\n\n<p><strong>2020\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shapelet\u3092\u7528\u3044\u305f\u682a\u4fa1\u306e\u30c8\u30ec\u30f3\u30c9\u4e88\u6e2c\uff08\u7e41\u6751\u5feb\u5fd7\uff09<\/li>\n\n\n\n<li>On The Asymptotic Monte Carlo Error for Stochastic Processes with Estimated Parameters\uff08Philip Beaucamp\uff09<\/li>\n\n\n\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u6b7b\u4ea1\u7387\u4e88\u6e2c\u306e\u4fee\u6b63\u3068\u30ea\u30b9\u30af\u30de\u30fc\u30b8\u30f3\u306e\u8a55\u4fa1\uff08\u5357\u512a\u5e0c\uff09<\/li>\n\n\n\n<li>\u7a7a\u9593\u4f9d\u5b58\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u30b5\u30a4\u30d0\u30fc\u30ea\u30b9\u30af\u8a55\u4fa1\uff08\u5e02\u5ddd\u771f\u540d\uff09<\/li>\n\n\n\n<li>\u53e4\u5178\u7684\u30ea\u30b9\u30af\u7406\u8ad6\u306b\u3088\u308b\u30b5\u30a4\u30d0\u30fc\u30a2\u30bf\u30c3\u30af\u306e\u30ea\u30b9\u30af\u8a55\u4fa1\uff08\u9577\u5c71\u5c1a\u5e73\uff09<\/li>\n\n\n\n<li>\u7a7a\u9593\u70b9\u904e\u7a0b\u306b\u304a\u3051\u308b\u30ab\u30fc\u30cd\u30eb\u63a8\u5b9a\u3068\u03c72\u9069\u5408\u5ea6\u691c\u5b9a\u3078\u306e\u5fdc\u7528\uff08\u5742\u672c\u5275\u6c70\uff09<\/li>\n\n\n\n<li>Foreign Exchange Rate Forecasting Using LongShort-Term Memory Model\uff08Chen Song\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2019\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>L\u00e9vy\u904e\u7a0b\u3092\u7528\u3044\u305f\u5909\u984d\u990a\u8001\u4fdd\u967a\u306b\u304a\u3051\u308b\u4fdd\u967a\u8ca0\u50b5\u8a55\u4fa1\uff08\u5ca1\u7530\u79c0\u8def\uff09<\/li>\n\n\n\n<li>\u30ec\u30f4\u30a3\u904e\u7a0b\u3092\u7528\u3044\u305f\u640d\u5bb3\u4fdd\u967a\u4f1a\u793e\u306e\u8ca0\u50b5\u8a55\u4fa1\uff08\u4e2d\u6751\u4eae\u592a\uff09<\/li>\n\n\n\n<li>\u69cb\u9020\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304f\u6b63\u5e38\u5fa9\u5e30\u3092\u8003\u616e\u3057\u305f\u671f\u5f85\u56de\u53ce\u984d\u8a55\u4fa1\uff08\u6c5f\u5c3b\u5145\u5e0c\uff09<\/li>\n\n\n\n<li>\u30ed\u30fc\u30bd\u30af\u8db3\u30c1\u30e3\u30fc\u30c8\u3092\u7528\u3044\u305fCNN\u306b\u3088\u308b\u682a\u4fa1\u4e88\u6e2c\uff08\u6797\u907c\u592a\u6717\uff09<\/li>\n\n\n\n<li>\u8aa4\u7279\u5b9a\u3092\u8003\u616e\u3057\u305f\u88fe\u306e\u91cd\u3044\u5206\u5e03\u306ePOT\u6cd5\u306b\u57fa\u3065\u304f\u95be\u5024\u63a8\u5b9a\uff08\u9ed2\u7530\u6d69\u55e3\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2018\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u4eee\u8aac\u306b\u57fa\u3065\u304f\u30b3\u30db\u30fc\u30c8\u5225\u6b7b\u4ea1\u7387\u63a8\u5b9a\u3068\u69cb\u9020\u30a2\u30d7\u30ed\u30fc\u30c1\uff08\u4f0a\u85e4\u9f8d\u4e4b\u4ecb\uff09<\/li>\n\n\n\n<li>\u78ba\u7387\u904e\u7a0b\u306e\u7d71\u8a08\u306b\u304a\u3051\u308b\u6700\u9069\u306a\u89b3\u6e2c\u5e45\u306e\u6c7a\u5b9a\uff08\u6817\u539f\u5eb7\u7950\uff09<\/li>\n\n\n\n<li>\u5c40\u6240\u5c24\u5ea6\u6cd5\u306b\u3088\u308b\u30f4\u30a1\u30a4\u30f3\u30fb\u30b3\u30d4\u30e5\u30e9\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u63a8\u5b9a\u3068\u305d\u306e\u6f38\u8fd1\u7684\u6027\u8cea\uff08\u9f4b\u85e4\u826f\u592a\uff09<\/li>\n\n\n\n<li>\u5916\u7684\u8981\u56e0\u3092\u7d44\u307f\u8fbc\u3093\u3060\u6b7b\u4ea1\u7387\u30e2\u30c7\u30eb\u306e\u8003\u6848\uff08\u4e2d\u6751\u96c4\u8cb4\uff09<\/li>\n\n\n\n<li>IBNR\u5099\u91d1\u304a\u3088\u3073RBNS\u5099\u91d1\u306b\u304a\u3051\u308b\u30af\u30ec\u30fc\u30e0\u4ef6\u6570\u306e\u540c\u6642\u4e88\u6e2c\uff08\u5bae\u5d0e\u590f\u5e06\uff09<\/li>\n\n\n\n<li>\u751f\u5b58\u6642\u9593\u89e3\u6790\u306e\u624b\u6cd5\u3092\u7528\u3044\u305f\u652f\u6255\u5099\u91d1\u306e\u63a8\u5b9a\uff08\u672c\u572d\u4f51\uff09<\/li>\n\n\n\n<li>\u7279\u7570\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u5b9f\u5bfe\u6570\u95be\u5024\u306e\u63a8\u5b9a\uff08\u7fa9\u6c38\u660e\u5927\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2017\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4e8c\u6b21\u5143\u30ea\u30b9\u30af\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u7834\u7523\u78ba\u7387\u306e\u8fd1\u4f3c\u8a08\u7b97\uff08\u5927\u897f\u5065\uff09<\/li>\n\n\n\n<li>Credibility Theory\u3092\u7528\u3044\u305f\u5c0f\u5730\u57df\u306e\u5c06\u6765\u6b7b\u4ea1\u7387\u4e88\u6e2c\uff08\u5c0f\u6797\u5468\u53f2\uff09<\/li>\n\n\n\n<li>\u30de\u30a4\u30af\u30ed\u30e2\u30c7\u30eb\u306b\u3088\u308b\u640d\u5bb3\u4fdd\u967a\u4e8b\u6545\u306b\u5bfe\u3059\u308bIBNR\u5099\u91d1\u8a55\u4fa1\uff08\u4f50\u3005\u6728\u5fb9\uff09<\/li>\n\n\n\n<li>\u88fe\u306e\u91cd\u3044\u5206\u5e03\u306e\u88fe\u8fd1\u4f3c\u3068POT\u6cd5\u306b\u304a\u3051\u308b\u4e00\u610f\u7684\u306a\u95be\u5024\u6c7a\u5b9a\u6cd5\uff08\u9234\u6728\u4f51\u8f14\uff09<\/li>\n\n\n\n<li>Parisian ruin\u306b\u304a\u3051\u308b\u7834\u7523\u78ba\u7387\u306e\u30ce\u30f3\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u63a8\u5b9a\uff08\u672c\u7530\u4e9c\u671b\uff09<\/li>\n\n\n\n<li>\u751f\u5b58\u6642\u9593\u5206\u6790\u3092\u7528\u3044\u305f\u652f\u6255\u5099\u91d1\u63a8\u5b9a\uff08\u5c71\u53e3\u5927\u5fd7\uff09<\/li>\n\n\n\n<li>A Study of Pricing American and Exotic Options using Least Squares Monte Carlo Method\uff08Anyi Yang\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2016\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Simulation-Based Inference for The Finite-Time Ruin Probability of A Surplus with A Long-Memory\uff08Yeteng Zheng\uff09<\/li>\n\n\n\n<li>\u7834\u7523\u78ba\u7387\u306e\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u63a8\u5b9a\u3068\u6f38\u8fd1\u63a8\u6e2c\u8ad6\uff08\u62bc\u76ee\u606d\u514b\uff09<\/li>\n\n\n\n<li>\u640d\u5bb3\u4fdd\u967a\u4e8b\u6545\u306e\u5831\u544a\u4ef6\u6570\u3092\u7528\u3044\u305f, IBNR\u5099\u91d1\u898b\u7a4d\u3082\u308a\u624b\u6cd5\u306e\u5177\u4f53\u7684\u306a\u63d0\u6848\uff08\u639b\u5ddd\u52dd\u5bdb\uff09<\/li>\n\n\n\n<li>\u5831\u544a\u4ef6\u6570\u3092\u7528\u3044\u305fIBNR\u5099\u91d1\u306e\u4fdd\u5b88\u7684\u306a\u898b\u7a4d\u3082\u308a\u624b\u6cd5\u306e\u63d0\u6848\uff08\u9ad8\u6a4b\u5feb\u58eb\uff09<\/li>\n\n\n\n<li>\u5831\u544a\u4ef6\u6570\u3092\u7528\u3044\u305fIBNR\u5099\u91d1\u8a55\u4fa1\u3068\u533b\u7642\u4fdd\u967a\u3078\u306e\u9069\u7528\uff08\u4e2d\u5e73\u572d\u4eae\uff09<\/li>\n\n\n\n<li>\u95be\u5024\u63a8\u5b9a\u6cd5\u3092\u7528\u3044\u305f\u682a\u4fa1\u904e\u7a0b\u306e\u63a8\u5b9a\uff08\u5897\u7530\u512a\u592a\uff09<\/li>\n\n\n\n<li>\u30af\u30ec\u30fc\u30e0\u5206\u5e03\u9078\u629e\u306e\u305f\u3081\u306e\u7834\u7523\u78ba\u7387\u306b\u57fa\u3065\u304f\u60c5\u5831\u91cf\u57fa\u6e96\uff08\u5bae\u6751\u5fc3\u5802\uff09<\/li>\n\n\n\n<li>The Ultimate Ruin Probability of A Perturbed Risk Process by Capital Injections\uff08Shufei Li\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2014\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30d7\u30ed\u30bb\u30b9\u30ea\u30b9\u30af\u3092\u8003\u616e\u3057\u305f\u30b3\u30a2\u9810\u91d1\u30e2\u30c7\u30eb\uff08\u91d1\u6d25\u5f18\u884c\uff09<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">\u5b66\u58eb<\/h4>\n\n\n\n<p><strong>2024\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30d9\u30a4\u30b8\u30a2\u30f3\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3088\u308b\u4e0d\u78ba\u5b9f\u6027\u306e\u5b9a\u91cf\u5316\u3068\u4fe1\u983c\u6027\u306e\u5411\u4e0a\uff08\u4e2d\u5ddd\u6d69\u8f14\uff09<\/li>\n\n\n\n<li>\u30d1\u30cd\u30eb\u30c7\u30fc\u30bf\u5206\u6790\u306b\u304a\u3051\u308b\u30e2\u30c7\u30eb\u9078\u629e\u306e\u624b\u6cd5\uff08\u4e2d\u539f\u667a\u54c9\uff09<\/li>\n\n\n\n<li>\u4e2d\u5fc3\u6975\u9650\u5b9a\u7406\u3068\u305d\u306e\u5fdc\u7528\uff08\u4e2d\u4e09\u5ddd\u7d17\u83dc\uff09<\/li>\n\n\n\n<li>M-\u63a8\u5b9a\u91cf\u306b\u304a\u3051\u308b\u4e00\u81f4\u6027\u3068\u6f38\u8fd1\u5206\u5e03\uff08\u5c71\u5d0e\u67ca\u4e1e\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2023\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6a5f\u68b0\u5b66\u7fd2\u306b\u304a\u3051\u308b\u524d\u51e6\u7406\u3068\u4ea4\u5dee\u691c\u8a3c\u306e\u56de\u6570\u304a\u3088\u3073\u63a2\u7d22\u56de\u6570\u306e\u59a5\u5f53\u6027\uff08\u6885\u6fa4\u6b63\u4eba\uff09<\/li>\n\n\n\n<li>\u30ea\u30b9\u30af\u5c3a\u5ea6\u306e\u8af8\u6027\u8cea\u3068\u6574\u5408\u6027\u306e\u6bd4\u8f03\u691c\u8a0e\uff08\u9577\u5ca1\u7406\u5b50\uff09<\/li>\n\n\n\n<li>\u4e00\u822c\u5316\u7dda\u5f62\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305fTPE\u63a8\u5b9a\uff08\u539f\u5149\u592a\u90ce\uff09<\/li>\n\n\n\n<li>\u975e\u8ca1\u52d9\u8cc7\u672c\u306e\u6570\u5024\u5316\u3068ESG-EBIT -\u67f3\u30e2\u30c7\u30eb\u3068\u81ea\u5df1\u5275\u8a2d\u306e\u308c\u3093-\uff08\u5c71\u4e0b\u76f4\u6597\uff09<\/li>\n\n\n\n<li>Mixed pre-conditioned Crank-Nicolson\u6cd5\u306e\u6570\u5024\u691c\u8a3c\u3068adaptive Bayes estimator\u306e\u6570\u5024\u8a08\u7b97\u3078\u306e\u5fdc\u7528\uff08\u82e5\u677e\u5b5d\u660e\uff09<\/li>\n\n\n\n<li>Basic element of Hawkes processes and statistical inference: an overview\uff08Xue Feng\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2022\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6975\u5024\u5206\u5e03\u306b\u304a\u3051\u308bFisher-Tippett\u306e\u5b9a\u7406\u3068\u6700\u5927\u5024\u5438\u5f15\u9818\u57df\uff08\u65b0\u4e95\u5eb7\u592a\uff09<\/li>\n\n\n\n<li>Newton\u6cd5\u3068\u6e96Newton\u6cd5\uff08\u5742\u4e0a\u7fd4\uff09<\/li>\n\n\n\n<li>\u96e2\u6563\u89b3\u6e2c\u3092\u7528\u3044\u305f\u30a8\u30eb\u30b4\u30fc\u30c9\u7684\u62e1\u6563\u904e\u7a0b\u306e\u30d1\u30e9\u30e1\u30c8\u30ea\u30c3\u30af\u63a8\u5b9a\u6cd5\uff08\u9234\u6728\u4fca\u592a\u90ce\uff09<\/li>\n\n\n\n<li>SARIMA\u30e2\u30c7\u30eb\u3068\u305d\u306e\u5b9f\u7528\u4f8b\uff08\u897f\u611b\u6a3a\uff09<\/li>\n\n\n\n<li>\u30ea\u30b9\u30af\u5c3a\u5ea6\u306e\u8af8\u6027\u8cea\u3068\u30c6\u30a4\u30eb\u30fb\u30ea\u30b9\u30af\uff08\u661f\u667a\u6d69\uff09<\/li>\n\n\n\n<li>\u30b1\u30ea\u30fc\u57fa\u6e96\u3092\u7528\u3044\u305f\u30ae\u30e3\u30f3\u30d6\u30eb\u3067\u306e\u8ced\u3051\u984d\u306e\u6700\u9069\u5316\uff08\u5c71\u91ce\u908a\u6566\u53f2\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2021\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u3068\u751f\u547d\u8868\u3092\u7528\u3044\u305f\u5e73\u5747\u4f59\u547d\u304a\u3088\u3073\u7d42\u8eab\u4fdd\u967a\u7d14\u4fdd\u967a\u6599\u306e\u8a08\u7b97\u3068\u6bd4\u8f03\uff08\u4e95\u4e0a\u307e\u3072\u308d\u30fb\u5149\u7530\u5927\u8f1d\uff09<\/li>\n\n\n\n<li>\u4fdd\u967a\u30ea\u30b9\u30af\u306e\u7d71\u8a08\u7684\u63a8\u6e2c\uff08\u6749\u5c71\u9686\u4eba\uff09<\/li>\n\n\n\n<li>\u30d0\u30ca\u30c3\u30cf\u7a7a\u9593\u306b\u304a\u3051\u308b\u4e8c\u3064\u306e\u5f31\u4f4d\u76f8\u306e\u69cb\u6210\u3068\u6027\u8cea\u304a\u3088\u3073\uff0c\u89d2\u8c37\u306e\u5b9a\u7406\uff08\u9ad8\u5ca1\u4f38\u65ec\uff09<\/li>\n\n\n\n<li>\u30ec\u30f4\u30a3\u904e\u7a0b\u3068\u305d\u306e\u751f\u6210\u4f5c\u7528\u7d20\u306b\u3064\u3044\u3066\uff08\u98db\u7530 \u7dbe\u4e5f\uff09<\/li>\n\n\n\n<li>\u78ba\u7387\u7a4d\u5206\u306e\u5b9a\u7fa9\u3068\u4f0a\u85e4\u306e\u516c\u5f0f\u306e\u8a3c\u660e\uff08\u5c71\u6839 \u4f51\u592a\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2020\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u533a\u9593\u30c7\u30fc\u30bf\u89e3\u6790\u3068\u30e2\u30c7\u30eb\u6bd4\u8f03\uff08\u5b5f\u662d\u56fd\uff09<\/li>\n\n\n\n<li>Ornstein=Uhlenbeck\u904e\u7a0b\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u63a8\u5b9a\u53ca\u3073\u91d1\u5229\u30e2\u30c7\u30eb\u3078\u306e\u5fdc\u7528\uff08\u9ad8\u4e0a\u96c4\u592a\u90ce\uff09<\/li>\n\n\n\n<li>\u6642\u9593\u9045\u308c\u3092\u3082\u3064\u78ba\u7387SIAR\u65b0\u578b\u30b3\u30ed\u30ca\u611f\u67d3\u75c7\u30e2\u30c7\u30eb\u306e\u5927\u57df\u89e3\u306e\u5b58\u5728\u3068\u4e00\u610f\u6027,\u304a\u3088\u3073\u6b63\u5024\u6027\uff08\u6839\u672c\u6ec9\u6689\uff09<\/li>\n\n\n\n<li>Random Forest in Japanese Second-hand TruckMarket with Limited Data\uff08Jiawei Guan\uff09<\/li>\n\n\n\n<li>\u78ba\u7387\u904e\u7a0b\u306b\u3088\u308b\u682a\u4fa1\u30c7\u30fc\u30bf\u306e\u30e2\u30c7\u30ea\u30f3\u30b0\u3068\u6a5f\u68b0\u5b66\u7fd2\u3092\u7528\u3044\u305f\u56de\u5e30\u5206\u6790\uff08\u9ad8\u6a4b\u667a\u4e5f\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2019\u5e74\u5ea6<\/strong> <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30ac\u30a6\u30b9\u904e\u7a0b\u6982\u8981\u53ca\u3073\u30ac\u30a6\u30b9\u904e\u7a0b\u3092\u7528\u3044\u305f\u6b7b\u4ea1\u7387\u4e88\u6e2c\uff08\u6cb3\u53e3\u771f\uff0c\u4f50\u85e4\u7ffc\uff09<\/li>\n\n\n\n<li>\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u6b7b\u4ea1\u7387\u4e88\u6e2c\u306b\u3064\u3044\u3066\uff08\u91d1\u4e95\u52c7\u6a39\uff09<\/li>\n\n\n\n<li>\u30ea\u30ab\u30ec\u30f3\u30c8\u30fb\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u6982\u8ad6\uff08\u4f50\u4e95 \u7ae0\u4eba\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2018\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u7279\u7570\u5024\u5206\u89e3\u3092\u7528\u3044\u305fLee-Carter model\u306b\u3088\u308b\u6b7b\u4ea1\u7387\u4e88\u6e2c\uff08\u5e02\u5ddd\u771f\u540d\uff09<\/li>\n\n\n\n<li>Determinantal Point Processes\u5165\u9580\uff08\u5742\u672c\u5275\u6c70\uff09<\/li>\n\n\n\n<li>\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u56de\u5e30\u4fc2\u6570\u306e\u63a8\u5b9a\uff08\u7e41\u6751\u5feb\u5fd7\uff09<\/li>\n\n\n\n<li>\u751f\u547d\u30a8\u30cd\u30eb\u30ae\u30fc\u30e2\u30c7\u30eb\u306b\u3088\u308b\u6b7b\u4ea1\u7387\u63a8\u5b9a\uff08\u5357\u512a\u5e0c\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2017\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u96e2\u6563\u578b\u304a\u3088\u3073\u9023\u7d9a\u578b\u30de\u30eb\u30c1\u30f3\u30b2\u30fc\u30eb\u306e\u7406\u8ad6\u3068\u5fdc\u7528\uff08\u6d77\u91ce\u767e\u5408\u5b50\uff09<\/li>\n\n\n\n<li>\u4f01\u696d\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u4fa1\u5024\u8a55\u4fa1\u306b\u304a\u3051\u308b\u4fa1\u5024\u5c3a\u5ea6\u306e\u5229\u7528\u6cd5\uff08\u6c5f\u5c3b\u5145\u5e0c\uff09<\/li>\n\n\n\n<li>\u30d6\u30e9\u30c3\u30af\uff1d\u30b7\u30e7\u30fc\u30eb\u30ba\u30fb\u30e2\u30c7\u30eb\u306b\u3088\u308b\u30aa\u30d7\u30b7\u30e7\u30f3\u4fa1\u683c\u5f0f\uff08\u6797\u907c\u592a\u6717\uff09<\/li>\n\n\n\n<li>Deduction of Black-Scholes Model\uff08Me Ming\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2016\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u78ba\u7387\u904e\u7a0b\u306e\u7d71\u8a08\u3068\u30a8\u30eb\u30b4\u30fc\u30c8\u6027\uff08\u6817\u539f\u5eb7\u7950\uff09<\/li>\n\n\n\n<li>\u4fe1\u983c\u6027\u7406\u8ad6\u306b\u3088\u308b\u63a8\u5b9a\u91cf\u306e\u7b97\u51fa\uff08\u9ad8\u5be6\u6893\uff09<\/li>\n\n\n\n<li>Lee-Carter model\u3092\u7528\u3044\u305f\u6b7b\u4ea1\u7387\u63a8\u5b9a\u3068\u65e5\u672c\u306b\u304a\u3051\u308b\u305d\u306e\u6709\u7528\u6027\uff08\u4e2d\u6751\u96c4\u8cb4\uff09<\/li>\n\n\n\n<li>\u9244\u9053\u4e8b\u6545\u81ea\u6bba\u30c7\u30fc\u30bf\u5206\u6790\u53ca\u3073xgboost\u306e\u89e3\u6790\u65b9\u6cd5\uff08\u91ce\u5c3b\u5cfb\u884c\uff09<\/li>\n\n\n\n<li>\u53e4\u5178\u7684\u7834\u7523\u7406\u8ad6\u306b\u304a\u3051\u308b\u7834\u7523\u78ba\u7387\u306e\u8a55\u4fa1\u306b\u3064\u3044\u3066\uff08\u672c\u572d\u4f51\uff09<\/li>\n\n\n\n<li>Deep Learning\u306b\u3088\u308b\u753b\u50cf\u8a8d\u8b58\uff08\u7fa9\u6c38\u660e\u5927\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>2015\u5e74\u5ea6<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>R\u8a00\u8a9e\u306b\u3088\u308b\u30af\u30ec\u30fc\u30e0\u30e2\u30c7\u30eb\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068IBNR\u5099\u91d1\u63a8\u5b9a\uff08\u4f50\u3005\u6728\u5fb9\uff09<\/li>\n\n\n\n<li>\u70ba\u66ff\u30ec\u30fc\u30c8\u306e\u5909\u52d5\u304c\u7dcf\u5408\u5546\u793e\u306b\u4e0e\u3048\u308b\u5f71\u97ff\u3068\u305d\u306e\u5206\u6790\uff08\u5cf6\u5d0e\u967d\u4ecb\uff09<\/li>\n\n\n\n<li>\u7834\u7523\u6642\u6b20\u640d\u984d\u306b\u5bfe\u3059\u308b\u4e0d\u5b8c\u5168\u5206\u5e03\u3068\u305d\u306e\u5fdc\u7528\uff08\u672c\u7530\u4e9c\u671b\uff09<\/li>\n\n\n\n<li>\u30d6\u30e9\u30c3\u30af-\u30b7\u30e7\u30fc\u30eb\u30ba\u30e2\u30c7\u30eb\u3068\u305d\u306e\u5fdc\u7528\uff08\u6a4b\u672c\u614e\uff09<\/li>\n\n\n\n<li>\u30d6\u30e9\u30c3\u30af-\u30b7\u30e7\u30fc\u30eb\u30ba\u30e2\u30c7\u30eb\u306e\u4e0d\u5b8c\u5168\u6027\uff08\u6e90\u672c\u609f\u53f8\uff09<\/li>\n\n\n\n<li>\u5927\u898f\u6a21\u707d\u5bb3\u4e0b\u3067\u306eIBNR\u5099\u91d1\u63a8\u5b9a\uff08\u5c71\u53e3\u5927\u5fd7\uff09\u200b<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n<ul class=\"wp-block-page-list\"><li class=\"wp-block-pages-list__item\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/academic-calendar\/\">Academic Calendar<\/a><\/li><li class=\"wp-block-pages-list__item\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/access-contact\/\">Access &amp; Contact<\/a><\/li><li class=\"wp-block-pages-list__item menu-item-home\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/\">Home<\/a><\/li><li class=\"wp-block-pages-list__item\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/members\/\">Members<\/a><\/li><li class=\"wp-block-pages-list__item has-child\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/research\/\">Research<\/a><ul class=\"wp-block-navigation__submenu-container\"><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/research\/competition\/\">Competition<\/a><\/li><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/research\/papers\/\">Papers<\/a><\/li><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/research\/presentations\/\">Presentations by students<\/a><\/li><\/ul><\/li><li class=\"wp-block-pages-list__item has-child\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/semproject\/\">SEM Project<\/a><ul class=\"wp-block-navigation__submenu-container\"><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/semproject\/japan\/\">Japan<\/a><\/li><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/semproject\/sweden\/\">Sweden<\/a><\/li><li class=\"wp-block-pages-list__item \"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/semproject\/usa\/\">USA<\/a><\/li><\/ul><\/li><li class=\"wp-block-pages-list__item\"><a class=\"wp-block-pages-list__item__link\" href=\"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/seminar\/\">Seminar<\/a><\/li><\/ul>","protected":false},"excerpt":{"rendered":"<p>\u7814\u7a76\u5ba4\u30e1\u30f3\u30d0\u30fc\u306b\u3088\u308b\u51fa\u7248\u8ad6\u6587 Works by Prof. Shimizu \u5352\u696d\u30fb\u5b66\u4f4d\u8ad6\u6587\uff08\u5b66\u58eb\u30fb\u4fee\u58eb\u30fb\u535a\u58eb\uff09 \u4fee\u58eb 2025\u5e74\u5ea6\uff089\u6708\u5352\u696d\uff09 2024\u5e74\u5ea6 2023\u5e74\u5ea6 2022\u5e74\u5ea6 2021\u5e74\u5ea6 2020\u5e74\u5ea6 201 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":538,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"vkexunit_cta_each_option":"","footnotes":""},"class_list":["post-1005","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/pages\/1005","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/comments?post=1005"}],"version-history":[{"count":63,"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/pages\/1005\/revisions"}],"predecessor-version":[{"id":2631,"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/pages\/1005\/revisions\/2631"}],"up":[{"embeddable":true,"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/pages\/538"}],"wp:attachment":[{"href":"https:\/\/www.shimizu.sci.waseda.ac.jp\/smzlab\/wp-json\/wp\/v2\/media?parent=1005"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}