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Stochastic differential equations an...
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Cheng, Chang-Hua.
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Stochastic differential equations and the method of sieves.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Stochastic differential equations and the method of sieves./
作者:
Cheng, Chang-Hua.
面頁冊數:
37 p.
附註:
Source: Masters Abstracts International, Volume: 42-03, page: 0948.
Contained By:
Masters Abstracts International42-03.
標題:
Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1416541
ISBN:
0496213261
Stochastic differential equations and the method of sieves.
Cheng, Chang-Hua.
Stochastic differential equations and the method of sieves.
- 37 p.
Source: Masters Abstracts International, Volume: 42-03, page: 0948.
Thesis (M.S.)--University of Southern California, 2003.
The time-dependent drift coefficient in a stochastic differential equation is estimated from the observations of the solution of the equation on a finite time interval. A linear non-stationary diffusion and a stochastic heat equation are considered. The estimator is a finite linear combination of cosine functions. While the existing theoretical results guarantee the convergence of the estimator to the actual drift coefficient, such results provide no information about performance of individual realizations of the estimator. With the help of computer simulations, it is shown in the current work that individual realizations of the estimator are sensitive to the initial condition of the observation process, and the performance of the estimator can be improved by increasing the initial condition. In the case of the stochastic heat equation, the performance of the estimator also depends on the size of the spatial domain.
ISBN: 0496213261Subjects--Topical Terms:
515831
Mathematics.
Stochastic differential equations and the method of sieves.
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The time-dependent drift coefficient in a stochastic differential equation is estimated from the observations of the solution of the equation on a finite time interval. A linear non-stationary diffusion and a stochastic heat equation are considered. The estimator is a finite linear combination of cosine functions. While the existing theoretical results guarantee the convergence of the estimator to the actual drift coefficient, such results provide no information about performance of individual realizations of the estimator. With the help of computer simulations, it is shown in the current work that individual realizations of the estimator are sensitive to the initial condition of the observation process, and the performance of the estimator can be improved by increasing the initial condition. In the case of the stochastic heat equation, the performance of the estimator also depends on the size of the spatial domain.
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