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過去の記録 ～06/22｜次回の予定｜今後の予定 06/23～
| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
2010年03月15日(月)
14:00-15:00 数理科学研究科棟(駒場) 002号室
Alexandre Brouste 氏 (Université du Maine)
Statistical inference in the partial observation setting, in continuous time
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/15.html
Alexandre Brouste 氏 (Université du Maine)
Statistical inference in the partial observation setting, in continuous time
[ 講演概要 ]
In various fields, the {\\it signal} process, whose law depends on an unknown parameter arthetainThetasubsetRp, can not be observed directly but only through an {\\it observation} process. We will talk about the so called fractional partial observation setting, where the observation process Y=left(Yt,tgeq0ight) is given by a stochastic differential equation: egin{equation} \\label{mod:modelgeneral} Y_t = Y_0 + \\int_0^t h(X_s, artheta) ds + \\sigma W^H_t\\,, \\quad t > 0 \\end{equation} where the function h:,RimesThetalongrightarrowR and the constant sigma>0 are known and the noise left(WHt,,tgeq0ight) is a fractional Brownian motion valued in R independent of the signal process X and the initial condition Y0. In this setting, the estimation of the unknown parameter arthetainTheta given the observation of the continuous sample path YT=left(Yt,0leqtleqTight), T>0, naturally arises.
[ 参考URL ]In various fields, the {\\it signal} process, whose law depends on an unknown parameter arthetainThetasubsetRp, can not be observed directly but only through an {\\it observation} process. We will talk about the so called fractional partial observation setting, where the observation process Y=left(Yt,tgeq0ight) is given by a stochastic differential equation: egin{equation} \\label{mod:modelgeneral} Y_t = Y_0 + \\int_0^t h(X_s, artheta) ds + \\sigma W^H_t\\,, \\quad t > 0 \\end{equation} where the function h:,RimesThetalongrightarrowR and the constant sigma>0 are known and the noise left(WHt,,tgeq0ight) is a fractional Brownian motion valued in R independent of the signal process X and the initial condition Y0. In this setting, the estimation of the unknown parameter arthetainTheta given the observation of the continuous sample path YT=left(Yt,0leqtleqTight), T>0, naturally arises.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/15.html
