EconPapers    
Economics at your fingertips  
 

Estimating Stochastic Dynamical Systems Driven by a Continuous-Time Jump Markov Process

Julien Chiquet () and Nikolaos Limnios ()
Additional contact information
Julien Chiquet: Université de Technologie de Compiègne, Centre de Recherche de Royallieu, LMAC
Nikolaos Limnios: Université de Technologie de Compiègne, Centre de Recherche de Royallieu, LMAC

Methodology and Computing in Applied Probability, 2006, vol. 8, issue 4, 431-447

Abstract: Abstract We discuss the use of a continuous-time jump Markov process as the driving process in stochastic differential systems. Results are given on the estimation of the infinitesimal generator of the jump Markov process, when considering sample paths on random time intervals. These results are then applied within the framework of stochastic dynamical systems modeling and estimation. Numerical examples are given to illustrate both consistency and asymptotic normality of the estimator of the infinitesimal generator of the driving process. We apply these results to fatigue crack growth modeling as an example of a complex dynamical system, with applications to reliability analysis.

Keywords: Stochastic dynamical system; Markov process; Estimation; Fatigue crack growth; 60H10; 60K40; 62M05 (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-006-0423-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:8:y:2006:i:4:d:10.1007_s11009-006-0423-z

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-006-0423-z

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:metcap:v:8:y:2006:i:4:d:10.1007_s11009-006-0423-z