On a state-space modelling for functional data Ortega-Moreno, Mónica Escabias Machuca, Manuel Karhunen–Loève expansion State-space models Kalman filter CAR(1) Random binary signal The objective of this paper is to derive a state-space model for several continuous-time processes, by applying the Karhunen–Loève expansion, and then to apply the Kalman filter equations. The accuracy of the models on the basis of deterministic or random inputs is studied by means of simulation on two well-known processes. 2022-03-15T11:38:54Z 2022-03-15T11:38:54Z 2007-03-17 info:eu-repo/semantics/article Ortega-Moreno, M., Escabias, M. On a state-space modelling for functional data. Computational Statistics 22, 429–438 (2007). https://doi.org/10.1007/s00180-007-0049-9 http://hdl.handle.net/10481/73446 https://doi.org/10.1007/s00180-007-0049-9 eng http://creativecommons.org/licenses/by-nd/3.0/es/ info:eu-repo/semantics/embargoedAccess Atribución-SinDerivadas 3.0 España Springer