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Title of Journal: Methodol Comput Appl Probab

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Abbravation: Methodology and Computing in Applied Probability

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Kluwer Academic Publishers

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DOI

10.1002/humu.22395

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1573-7713

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Estimating Stochastic Dynamical Systems Driven by

Authors: Julien Chiquet Nikolaos Limnios
Publish Date: 2006/12/01
Volume: 8, Issue: 4, Pages: 431-447
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Abstract

We discuss the use of a continuoustime 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

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Other Papers In This Journal:
  1. Effect of Data Transformations on Predictive Risk Indicators
  2. Initial and Final Backward and Forward Discrete Time Non-homogeneous Semi-Markov Credit Risk Models
  3. Random Databases with Approximate Record Matching
  4. Importance and Sensitivity Analysis in Dynamic Reliability
  5. Random Survival Forests Models for SME Credit Risk Measurement
  6. Selecting the Best Population Using a Test for Equality Based on Minimal Wilcoxon Rank-sum Precedence Statistic
  7. Saddlepoint Approximations to Tail Probabilities and Quantiles of Inhomogeneous Discounted Compound Poisson Processes with Periodic Intensity Functions
  8. On Estimation of the Intensity Function of a Point Process
  9. Call Option Prices Based on Bessel Processes
  10. Goodness-of-Fit Tests for Continuous Regression
  11. Waiting Time Distribution for the Emergence of Superpatterns
  12. Computing the Expected Markov Reward Rates with Stationarity Detection and Relative Error Control
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