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Nonparametric multi-step prediction in nonlinear state space dynamic systems

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  • Vila, Jean-Pierre

Abstract

Filtering and smoothing of stochastic state space dynamic systems have benefited from several generations of estimation approaches since the seminal works of Kalman in the sixties. A set of global analytical or numerical methods are now available, such as the well-known sequential Monte Carlo particle methods which offer some theoretical convergence results for both types of problems. However except in the case of linear Gaussian systems, objectives of the third kind i.e. prediction objectives, which aim at estimating k time steps ahead the anticipated probability density function of the system state variables, conditional on past and present system output observations, still raise theoretical and practical difficulties. The aim of this paper is to propose a nonparametric particle multi-step prediction method able to consistently estimate such anticipated conditional pdf of the state variables as well as their expectations.

Suggested Citation

  • Vila, Jean-Pierre, 2011. "Nonparametric multi-step prediction in nonlinear state space dynamic systems," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 71-76, January.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:1:p:71-76
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    Cited by:

    1. Vila, Jean-Pierre, 2012. "Enhanced consistency of the Resampled Convolution Particle Filter," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 786-797.

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