Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Rodi Lykou and George Tsaklidis
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Rodi Lykou: Department of Statistics and Operational Research, School of Mathematics, Aristotle University of Thessaloniki, GR54124 Thessaloniki, Greece
George Tsaklidis: Department of Statistics and Operational Research, School of Mathematics, Aristotle University of Thessaloniki, GR54124 Thessaloniki, Greece

Mathematics, 2021, vol. 9, issue 12, 1-16

Abstract: Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.

Keywords: particle filter; missing data; single imputation; impoverishment; Markov Systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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