Functional kriging: Concurrent model
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Autores: Ramón Giraldo, Pedro Delicado
, Jorge Mateu Mahiques
- Localización: XXX Congreso Nacional de Estadística e Investigación Operativa y de las IV Jornadas de Estadística Pública: actas, 2007, ISBN 978-84-690-7249-3
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Since beginning of the nineties the statistical community has been interested in developing models for functional data. Functional versions for many branches of statistics have been given. Examples of such methods include exploratory data analysis, linear models, longitudinal data or multivariate techniques. In the same way that standard statistical methods have been generalized to be used with functional data, it is possible to think that geostatistical methods can be adapted to these type of data. In this work we propose a methodology to carry out spatial prediction when measured data are curves. Our approach is based on the functional linear concurrent model theory. The spatial prediction of an unobserved curve is obtained as a linear combination of observed functions.
We adapt an optimization criterium used in multivariable spatial prediction to estimate the kriging parameters. We use the Canadian temperature data set showed in Ramsay and Silverman (2005) to illustrate the proposals.
