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<論文＞
A. Jasra, K. Kamatani, and H. Masuda. Bayesian inference for Stable Levy driven stochastic differential equations with high-frequency data, Accepted at SJS
Brouste, A., Masuda, H.: Efficient estimation of stable Levy process with symmetric jumps. Statistical Inference for Stochastic Processes, Volume 21,Issue 2, 289-307 (2018).[doi: 10.1007/s11203-018-9181-0]
Hayashi, T., Koike, Y.: Wavelet-based methods for high-frequency lead-lag analysis. SIAM Journal on Financial Mathematics, Volume 9, Issue 4, 1208-1248 (2018).
Iwasaki, T., Hino, H., Tatsuno, M., Akaho, S., Murata, N.:Estimation of neural connections from partially observed neural spikes, Neural Networks, Volume 108, 172-191 (2018)
Kaino, Y. and Uchida, M. (2018b). Hybrid estimators for stochastic differential equations from reduced data. Special 20th anniversary issue. Statistical Inference for Stochastic Processes, Volume 21, Issue 2, 435-454.
Kaino, Y. and Uchida, M. (2018a). Hybrid estimators for small diffusion processes based on reduced data. Metrika, Volume 81, Issue 7, 745-773.
Koike Y.: Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data.Annals of Statistics, Volume 47, Issue 3, 1663-1687 (2019).
Koike, Y., Tanoue, Y.: Oracle inequalities for sign constrained generalized linear models. Econometrics and Statistics, to appear.
Koike, Y.: Mixed-normal limit theorems for multiple Skorohod integrals in high-dimensions, with application to realized covariance. Electronic Journal of Statistics, to appear.
Masuda, H.: Non-Gaussian quasi-likelihood estimation of SDE driven by locally stable Levy process. Stochastic Processes and their Applications, Volume 129, Issue 3, 1013-1059 (2019).[doi: 10.1016/j.spa.2018.04.004] arXiv:1608.06758
Oshime, T., Shimizu, Y.: Parametric inference for ruin probability in the classical risk model, Statistics and Probability Letters, vol. 133,28-37, 2018.
Shimizu, Y., Tanaka, S.: Dynamic risk measures for stochastic asset processes from ruin theory, Annals of Actuarial Science, vol. 12, no. 2,249-268, 2018.
Sonoda, S., Nakamura, K., Kaneda, Y., Hino, H., Akaho, S., Murata, N.,Miyauchi, E., Kawasaki, M.: EEG dipole source localization with information criteria for multiple particle filters, Neural Networks,Volume 108, 68-82 (2018)
Suzuki, T.: Fast Learning Rate of Non-Sparse Multiple Kernel Learning and Optimal Regularization Strategies. Electronic Journal of Statistics, Volume 12, Number 2 (2018), 2141--2192.
Suzuki, T.: Fast generalization error bound of deep learning from a kernel perspective. AISTATS2018, Proceedings of Machine Learning Research, 84:1397--1406, 2018.
Mori, Y.and Taiji Suzuki: Generalized ridge estimator and model selection criteria in multivariate linear regression. Journal of Multivariate Analysis, volume 165, pages 243--261, May 2018.
Yoshida, N.:Partial quasi likelihood analysis.Japanese Journal of Statistics and Data Science.June 2018, Volume 1, Issue 1, pp 157-189. https://doi.org/10.1007/s42081-018-0006-6
＜学会発表＞
Kamatani, K.: Bayesian inference for stable Levy driven stochastic differential equations with high-frequency data, ERCIM 2018, Pisa, Italy, 15th December.
Koike, Y.: Testing the absence of lead-lag effects in high-frequency data. EcoSta 2018. Hong-Kong, China,2018.6.19.
Koike, Y.: Asymptotic Mixed Normality of Realized Covariance in High-Dimensions. IMS-APRM 2018. Singapole,2018.6.29.
Koike, Y.: Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data.10th World Congress of the Bachelier Finance Society. Dublin, Ireland, 2018.7.17.
Koike, Y.: Testing the Residual Sparsity of a High-Dimensional Continuous-Time Factor Model. CEQURA Conference 2018. Munich, Germany, 2018.10.4.
Koike, Y.: Testing the residual sparsity of a high-dimensional continuous-time factor model. CMStatistics 2018.Pisa, Italy, 2018.12.15.
Koike, Y.: On implementation of high-dimensional covariance estimation in YUIMA package. 4th Yuima Users Workshop. Tokyo, Japan, 2019.1.29.
Koike, Y.: De-biasing the graphical Lasso in high-frequency data. ASC2019. Tokyo, Japan, 2019.1.30.
Koike, Y.: High-dimensional covariance estimation in YUIMA package. The 2nd YUIMA Conference, Rome,Italy, 2018.3.25.
Masuda, H.: Optimal stable regression. APRM 2018, Singapore, 2018.6.29.
Masuda, H.: Locally stable regression with unknown activity index. CMStatistics 2018, Pisa, Italy, 2018.12.15.
Shimizu, Y.: Asymptotically normal estimators of ruin probability under Levy insurance risks, The 22th International congress on Insurance:Mathematics and Economics, Sydney, Australia, July 16, 2018
Shimizu, Y.: A dynamic risk measure from Ruin Theory: Gerber-Shiu analysis, CEQURA Conference on Advances in Financial and Insurance Risk Management, Munich, Germany, October 4, 2018
Shimizu, Y.: Asymptotically normal estimators of ruin probability under Levy insurance risks, CFE-CMStatistics 2018, Pisa, Italy, December 15, 2018
Suzuki, T.： Estimating nonlinear tensor product in infinite dimensional functional space by kernel and neural network models.IMS-APRM2018. Oral presentation．26th/Jun/2018. National University of Singapore.
Yoshida, N.:Quasi Likelihood Analysis Of Ratio Models And Limit Order Book.Dynstoch 2018.Porto, Portugal. 2018.6.7.
Yoshida, N.:Partial quasi likelihood analysis.The 5th Institute of Mathematical Statistics Asia Pacifim Rim Meeting (APRM). National University of Singapore.2018.6.29.
Yoshida, N.:Approaches to asymptotic expansion. Asymptotic Expansion and Malliavin Calculus. Paris, France. 2018.11.16.
Yoshida, N.:Global jump filters and quasi likelihood analysis for volatility.CMStatistics 2018. Pisa, Italy.2018.12.15.
Yoshida, N.:Global jump filters and quasi likelihood analysis for volatility. ASC2019: Asymptotic Statistics and Computations. Tokyo, Japan. 2019.1.30.
Yoshida, N.:Asymptotic expansion revisited: toward reconstruction of the asymptotic term. The Second YUIMA Conference. Rome, Italy. 2019.3.22.
＜プレプリント＞
Eguchi, S., Masuda, H.: Data driven time scale in Gaussian quasi-likelihood inference, to appear in Statistical Inference for Stochastic Processes, arXiv:1801.10378.
Jasra, A., Kaamatani, K., Masuda, H.: Bayesian inference for stable Levy driven stochastic differential equations with high-frequency data, to appear in Scandinavian Journal of Statistics, arXiv:1707.08788
Koike, Y.: High-dimensional central limit theorems for homogeneous sums. arXiv:1902.03809 (2018).
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