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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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Springer US

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DOI

10.1002/ana.410230713

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1573-7713

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Importance and Sensitivity Analysis in Dynamic Rel

Authors: Robert Eymard Sophie Mercier Michel Roussignol
Publish Date: 2009/02/03
Volume: 13, Issue: 1, Pages: 75-104
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Abstract

In dynamic reliability the evolution of a system is governed by a piecewise deterministic Markov process which is characterized by different input data Assuming such data to depend on some parameter p ∈ P our aim is to compute the firstorder derivative with respect to each p ∈ P of some functionals of the process which may help to rank input data according to their relative importance in view of sensitivity analysis The functionals of interest are expected values of some function of the process cumulated on some finite time interval 0t and their asymptotic values per unit time Typical quantities of interest hence are cumulated production availability or mean number of failures on some finite time interval and similar asymptotic quantities The computation of the firstorder derivative with respect to p ∈ P is made through a probabilistic counterpart of the adjoint state method from the numerical analysis field Examples are provided showing the good efficiency of this method especially in case of a large P

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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. Random Survival Forests Models for SME Credit Risk Measurement
  5. Selecting the Best Population Using a Test for Equality Based on Minimal Wilcoxon Rank-sum Precedence Statistic
  6. Saddlepoint Approximations to Tail Probabilities and Quantiles of Inhomogeneous Discounted Compound Poisson Processes with Periodic Intensity Functions
  7. On Estimation of the Intensity Function of a Point Process
  8. Call Option Prices Based on Bessel Processes
  9. Goodness-of-Fit Tests for Continuous Regression
  10. Waiting Time Distribution for the Emergence of Superpatterns
  11. Computing the Expected Markov Reward Rates with Stationarity Detection and Relative Error Control
  12. Estimating Stochastic Dynamical Systems Driven by a Continuous-Time Jump Markov Process
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