On a state-space modelling for functional data

Ortega-Moreno, Mónica; Escabias Machuca, Manuel

doi:https://doi.org/10.1007/s00180-007-0049-9

Preprint On a state-space modelling for functional data (338.5Ko)

Identificadores

URI: http://hdl.handle.net/10481/73446

DOI: https://doi.org/10.1007/s00180-007-0049-9

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Editorial

Springer

Materia

Karhunen–Loève expansion

State-space models

Kalman filter

CAR(1)

Random binary signal

Date

2007-03-17

Referencia bibliográfica

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

Patrocinador

Project MTM2004-5992 of Dirección General de Investigación del Ministerio de Ciencia y Tecnología of Spain and the Research Group FQM307 financed by III-PAI of Conserjería de Educación y Ciencia de la Junta de Andalucía

Résumé

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.

Colecciones

FQM307 - Artículos

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