Abstract
This paper addresses the problem of reconstructing partially observed stochastic processes. The L1 convergence of the filtering and smoothing densities in state space models is studied, when the transition and emission densities are estimated using non parametric kernel estimates. An application to real data is proposed, in which a wave time series is forecasted given a wind time series.
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Valérie Monbet—supported by IFREMER, Brest, France.
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Monbet, V., Ailliot, P. & Marteau, PF. L1-convergence of smoothing densities in non-parametric state space models. Stat Infer Stoch Process 11, 311–325 (2008). https://doi.org/10.1007/s11203-007-9020-1
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DOI: https://doi.org/10.1007/s11203-007-9020-1