| 書目名稱 | Identification of Dynamical Systems with Small Noise | | 編輯 | Yu. Kutoyants | | 視頻video | http://file.papertrans.cn/461/460835/460835.mp4 | | 叢書名稱 | Mathematics and Its Applications | | 圖書封面 |  | | 描述 | Small noise is a good noise. In this work, we are interested in the problems of estimation theory concerned with observations of the diffusion-type process Xo = Xo, 0 ~ t ~ T, (0. 1) where W is a standard Wiener process and St(‘) is some nonanticipative smooth t function. By the observations X = {X , 0 ~ t ~ T} of this process, we will solve some t of the problems of identification, both parametric and nonparametric. If the trend S(-) is known up to the value of some finite-dimensional parameter St(X) = St((}, X), where (} E e c Rd , then we have a parametric case. The nonparametric problems arise if we know only the degree of smoothness of the function St(X), 0 ~ t ~ T with respect to time t. It is supposed that the diffusion coefficient c is always known. In the parametric case, we describe the asymptotical properties of maximum likelihood (MLE), Bayes (BE) and minimum distance (MDE) estimators as c --+ 0 and in the nonparametric situation, we investigate some kernel-type estimators of unknown functions (say, StO,O ~ t ~ T). The asymptotic in such problems of estimation for this scheme of observations was usually considered as T --+ 00 , because this limit is a direct analog to t | | 出版日期 | Book 1994 | | 關(guān)鍵詞 | Estimator; Likelihood; Parameter; Probability theory; Rang; communication; disorder problem; dynamical syst | | 版次 | 1 | | doi | https://doi.org/10.1007/978-94-011-1020-4 | | isbn_softcover | 978-94-010-4444-8 | | isbn_ebook | 978-94-011-1020-4 | | copyright | Springer Science+Business Media Dordrecht 1994 |
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