Model robustness of finite state nonlinear filtering over the infinite time horizon

Pavel Chigansky, Ramon Van Handel

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We investigate the robustness of nonlinear filtering for continuous time finite state Markov chains, observed in white noise, with respect to misspecification of the model parameters. It is shown that the distance between the optimal filter and that with incorrect model parameters converges to zero uniformly over the infinite time interval as the misspecified model converges to the true model, provided the signal obeys a mixing condition. The filtering error is controlled through the exponential decay of the derivative of the nonlinear filter with respect to its initial condition. We allow simultaneously for misspecification of the initial condition, of the transition rates of the signal, and of the observation function. The first two cases are treated by relatively elementary means, while the latter case requires the use of Skorokhod integrals and tools of anticipative stochastic calculus.

Original languageEnglish (US)
Pages (from-to)688-715
Number of pages28
JournalAnnals of Applied Probability
Volume17
Issue number2
DOIs
StatePublished - Apr 1 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Anticipative stochastic calculus
  • Error bounds
  • Filter stability
  • Markov chains
  • Model robustness
  • Nonlinear filtering

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