Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/15570
Title: Bias-correction of Kalman filter estimators associated to a linear state space model with estimated parameters
Author: Costa, Marco
Monteiro, Magda
Keywords: State space model
Kalman filter
Estimated parameters
Bias-correction
Predictions bias
Environmental data
Issue Date: Apr-2016
Publisher: Elsevier
Abstract: This paper aims to discuss some practical problems on linear state space models with estimated parameters. While the existing research focuses on the prediction mean square error of the Kalman filter estimators, this work presents some results on bias propagation into both one-step ahead and update estimators, namely, non recursive analytical expressions for them. In particular, it is discussed the impact of the bias in the invariant state space models. The theoretical results presented in this work provide an adaptive correction procedure based on any parameters estimation method (for instance, maximum likelihood or distribution-free estimators). This procedure is applied to two data set: in the calibration of radar precipitation estimates and in the global mean land–ocean temperature index modeling.
Peer review: yes
URI: http://hdl.handle.net/10773/15570
DOI: 10.1016/j.jspi.2016.04.002
ISSN: 0378-3758
Appears in Collections:ESTGA - Artigos

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