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 Parameter estimation of state space models for univariate observations
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/8410

title: Parameter estimation of state space models for univariate observations
authors: Costa, Marco
Alpuim, Teresa
keywords: Kalman filter
State space model
Parameters estimation
Area rainfall estimates
issue date: Jul-2010
publisher: Elsevier
abstract: This paper contributes to the problem of estimation of state space model parameters by proposing estimators for the mean, the autoregressive parameters and the noise variances which, contrarily to maximum likelihood, may be calculated without assuming any specific distribution for the errors. The estimators suggested widen the scope of the application of the generalized method of moments to some heteroscedastic models, as in the case of state-space models with varying coefficients, and give sufficient conditions for their consistency. The paper includes a simulation study comparing the proposed estimators with maximum likelihood estimators. Finally, these methods are applied to the calibration of the meteorological radar and estimation of area rainfall.
URI: http://hdl.handle.net/10773/8410
ISSN: 0378-3758
publisher version/DOI: http://dx.doi.org/10.1016/j.jspi.2010.01.036
source: Journal of Statistical Planning and Inference
appears in collectionsESTGA - Artigos

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