On a state-space modelling for functional data (Q964620)
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scientific article; zbMATH DE number 5697299
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | On a state-space modelling for functional data |
scientific article; zbMATH DE number 5697299 |
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On a state-space modelling for functional data (English)
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22 April 2010
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State-space models are built for continuous-time first order autoregression processes and random binary signals. They are based on the Karhunen-Loève orthogonal expansion. The Kalman filtering method is applied for prediction of the processes. Results of simulations are presented.
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continuous time autoregression
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Karhunen-Loeve expansion
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filtering
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