 Nonparametric multi-step prediction in nonlinear state space dynamic systemsJean-Pierre Vila Statistics & Probability Letters, 2011, vol. 81, issue 1, 71-76 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. Keywords: State; space; dynamic; systems; Prediction; Filtering; Smoothing; Kernel; density; estimators (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:81:y:2011:i:1:p:71-76 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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