東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Numerical Methods for Parameter Esti...

Clifton, Cody E.

Linked to FindBook

Google Book

Amazon

博客來

Numerical Methods for Parameter Estimation in Stochastic Systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Numerical Methods for Parameter Estimation in Stochastic Systems./
Author:	Clifton, Cody E.
Description:	72 p.
Notes:	Source: Masters Abstracts International, Volume: 52-01.
Contained By:	Masters Abstracts International52-01(E).
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1540317
ISBN:	9781303178948

Numerical Methods for Parameter Estimation in Stochastic Systems.
Clifton, Cody E.

Numerical Methods for Parameter Estimation in Stochastic Systems. - 72 p.

Source: Masters Abstracts International, Volume: 52-01.

Thesis (M.A.)--University of Kansas, 2013.

The objective of this work is to provide numerical simulations in support of a collection of existing results on estimation in two distinct types of stochastic systems.

ISBN: 9781303178948Subjects--Topical Terms:

1669109
Applied Mathematics.

Numerical Methods for Parameter Estimation in Stochastic Systems.
LDR:02384nam a2200301 4500 001 1960389
005 20140616133307.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303178948
035 $a (MiAaPQ)AAI1540317
035 $a AAI1540317
040 $a MiAaPQ $c MiAaPQ
100 1 $a Clifton, Cody E. $3 2096036
245 1 0 $a Numerical Methods for Parameter Estimation in Stochastic Systems.
300 $a 72 p.
500 $a Source: Masters Abstracts International, Volume: 52-01.
500 $a Adviser: Bozenna Pasik-Duncan.
502 $a Thesis (M.A.)--University of Kansas, 2013.
520 $a The objective of this work is to provide numerical simulations in support of a collection of existing results on estimation in two distinct types of stochastic systems.
520 $a In the first chapter, we consider a linear time-invariant higher-order system of order that is subject to white noise perturbation. We numerically illustrate the result that the quadratic variation estimator of the white noise local variance is asymptotically biased when a forward-difference approach is used for numerically approximating the derivatives of the stochastic process, and that the bias can be eliminated by instead applying a specific alternative numerical differentiation scheme. Moreover, we consider the result that the straightforward discretization of a least squares estimation procedure for unknown parameters in the system leads to an asymptotically biased estimate.
520 $a In the second chapter, we consider a controlled Markov chain, taking values on a finite state space, whose transition probabilities are assumed to depend on an unknown parameter belonging to a compact set. We first provide numerical illustration of the result that under a particular identifiability condition, the maximum likelihood estimator of this parameter is strongly consistent. Next, we illustrate that under alternative assumptions the sequence of maximum likelihood estimates converges and retains a desirable property relating to the Markov chain's transition probabilities. Additionally, we present a survey of several other related results.
590 $a School code: 0099.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Mathematics. $3 515831
690 $a 0364
690 $a 0405
710 2 $a University of Kansas. $b Mathematics. $3 1266670
773 0 $t Masters Abstracts International $g 52-01(E).
790 $a 0099
791 $a M.A.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1540317