|
<論文発表＞
Podolskij, M., Yoshida. N.: Edgeworth expansion for functionals of continuous diffusion processes, Annals of Applied Probability, Volume 26, Number 6 (2016), 3415-3455.
Kimura, A., Yoshida, N.: "Estimation of correlation between latent processes", Jan Kallsen, Antonis Papapantoleon (eds) Advanced Modelling in Mathematical Finance: In Honour of Ernst Eberlein. Springer, 131-146 (2016)
Yoshida, N.":Asymptotic Expansions for Stochastic Processes", ,M. Denker, E. Waymire (eds) Rabi N. Bhattacharya: Selected Papers. 15−3, Springer (2016)
Clinet, S. and Yoshida, N. "Statistical Inference for Ergodic Point Processes and Application to Limit Order Book". To appear in Stochastic Processes and Their Applications (2016)
Muni Toke I. and Yoshida N., "Modelling intensities of order flows in a limit order book", Quantitative Finance, in press, (2017) (online version 2016)
Podolskij, M., Veliyev, B., Yoshida, N.: "Edgeworth expansion for the pre-averaging estimator" Stochastic Processes and Their Applications. Accepted.
Iacus, S.M., Yoshida, N, "Simulation and Inference for Stochastic Processes with YUIMA", Springer. In press.
Nomura, R. and Uchida, M. (2016). Adaptive Bayes estimators and hybrid estimators for small diffusion processes based on sampled data. Journal of the Japan Statistical Society, 46, no. 2, 129-154.
Song Liu, Taiji Suzuki, Relator Raissa, Jun Sese, Masashi Sugiyama,and Kenji Fukumizu:Support Consistency of Direct Sparse-Change Learning in Markov Networks.The Annals of Statistics, 2017 (accepted).
Song Liu, Kenji Fukumizu and Taiji Suzuki:Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories. Behaviormetrika.Volume 44, Issue 1, pp 265-286, 2017.
Taiji Suzuki and Heishiro Kanagawa:Bayes method for low rank tensor estimation.
Journal of Physics: Conference Series, 699(1), pp. 012020, 2016.
Taiji Suzuki, Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu,and Yukihiro Tagami: Minimax Optimal Alternating Minimization forKernel Nonparametric Tensor Learning. The 30th Annual Conference onNeural Information Processing Systems (NIPS2016), pp. 3783-3791, 2016.
Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu,and Yukihiro Tagami: Gaussian process nonparametric tensor estimatorand its minimax optimality. Proceedings of The 33rd InternationalConference on Machine Learning, pp. 1632-1641, 2016.
Hino, H., Akaho, S. & Murata, N.: An entropy estimator based on polynomial regression with poisson error structure. ICONIP 2016 (23rd International Conference on Neural Information Processing), Vol. 9948 LNCS, 11-19 (2016)
Takano, K., Hino, H., Akaho, S. & Murata, N.: Nonparametric e-mixture estimation. Neural Computation, 28(12), 2687-2725 (2016)
Koshijima, K., Hino, H. & Murata, N.: Change-point detection in a sequence of bags-of-data. ICDE 2016 (IEEE 32nd International Conference on Data Engineering), 1560-1561 (2016)
Eguchi, S. and Masuda, H.:Schwarz type model comparison for LAQ models. arXiv:1606.01627, to appear in Bernoulli
上原悠槙, 増田弘毅: Levy駆動型確率微分方程式の段階的推定について. 統計数理，採択済み
Masuda, H. and Uehara, Y.:Two-step estimation of ergodic Levy driven SDE. Statistical Inference for Stochastic Processes 20 (2017), 105-137. [doi: 10.1007/s11203-016-9133-5]
Kamatani, K.: Ergodicity of Markov chain Monte Carlo with reversible proposal. Journal of Applied Probability, no. 54, (2017).
Long, H., Ma, C., Shimizu, Y.: Least squares estimators for stochastic differential equations driven by small Levy noises, Stochastic Processes and their Applications. 127, no.5, 1475-1495 (2017)
Feng, R., Shimizu, Y.: Applications of central limit theorems for equity-linked insurance. Insurance: Mathematics and Economics, 69, 138-148 (2016)
深澤正彰「高頻度データに対する Whittle 推定」統計数理 65-1 掲載予定
小池祐太: 実現ボラティリティとその周辺. 経営と制度, 15 号, 15-42 (2017).
<学会発表＞
Yoshida, N.: Quasi likelihood analysis and limit order book modeling. Statistical methods for dynamical stochastic models (DYNSTOCH 2016). University Rennes 2. 2016.6.8.
Yoshida, N.: Statistics for stochastic processes: inferential and probabilistic aspects. The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, The Chinese University of Hong Kong. 2016.6.30.
Yoshida, N.: Point processes and limit order book modeling. World Congress in Probability and Statistics. King's College Circle, Canada. 2016.7.13.
Yoshida, N.: Recent developments in asymptotic expansion for non-ergodic systems. Advances in Statistics for Random Processes. University of Maine, Le Mans, France. 2016.9.7.
Yoshida, N.: Applications of the quasi-likelihood analysis for point processes to high frequency. 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016). University of Seville, Spain. 2016.12.10.
Yoshida, N.: Limit order book modeling and quasi likelihood analysis. Workshop on Stochastic Models, Statistics and Their Applications. Humboldt University of Berlin. 2017.2.21.
Shimizu, Y.: Applications of central limit theorems for equity-linked insurance,
Waseda International Symposium 2017, Waseda University, February 27 - March 1, 2017.
Shimizu, Y.: Applications of central limit theorems for equity-linked insurance,
ASC2017: Asymptotic Statistics and Computations, The University of Tokyo, January 30 - February 1, 2017
清水泰隆: Dynamic risk measures for stochastic asset processes from ruin theory,
経済リスクの統計学の新展開：稀な事象と再起的事象, 東京大学＠本郷, 平成28年12月22日
Shimizu, Y.: Simulation-based inference for the finite-time ruin probability of a surplus with long-memory,The 20th International congress on Insurance: Mathematics and Economics, Atlanta, USA, 24-27 July, 2016
高畠哲也, 深澤正彰 「フラクショナル確率ボラティリティモデルに対する高頻度データ解析」統計関連学会連合大会 2016年9月6日
小池祐太, 「超高頻度データに対するリード・ラグ効果の推定について」, 慶應義塾大学経済研究所計量経済学ワークショップ, 慶應義塾大学三田キャンパス, 2016 年5月10日.
Y. Koike．：Statistical analysis of price discovery: a stochastic process approach, IMS-APRM2016, The Chinese University of Hong Kong, Hong-Kong, 2016 年6月30日.
小池祐太, 「高頻度時系列データに対するリード・ラグ効果の統計解析」, 2016 年度統計関連学会連合大会, 金沢大学角間キャンパス, 2016 年9月6日.
小池祐太, 「リードラグ効果のウェーブレット解析」, 大規模統計モデリングと計算統計III, 東京大学駒場キャンパス, 2016 年9 月28 日.
Y. Koike.: Quadratic Variation Estimation of an Irregularly Observed Semimartingale with Jumps and Noise. Wakimoto Memorial Session 2016, Statistical training center, Daejeon, Korea, 2016 年11 月5 日.
Y. Koike.: Time varying lead-lag effect. CFE-CMStatistics 2016, University of Seville, Seville, Spain, 2016 年12 月10 日.
Y. Koike.: Capturing heterogeneous lead-lag effects from ultra high frequency data", ASC2017, University of Tokyo, Tokyo, Japan, 2017年2月1日.
Y. Koike.: Inference for time-varying lead-lag relationships from ultra high frequency data. FST seminar, University of Macau, Macau, China, 2017年3月15日.
＜プレプリント＞
Shimizu, Y., Tanaka, S.: Dynamic risk measures for stochastic asset processes from ruin theory, preprint (submitted), 2017.
Hayashi, T., Koike, Y.: Wavelet-based methods for high-frequency lead-lag analysis. Working paper. Available at arXiv: https://arxiv.org/abs/1612.01232 (2016).
Koike, Y., Liu, Z.: Asymptotic properties of the realized skewness and related statistics. Working paper. Available at arXiv: https://arxiv.org/abs/1612.08526 (2016).
Koike, Y.: Inference for time-varying lead-lag relationships from ultra high-frequency data. Working paper. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract id=2924301 (2017).
|