A penalized likelihood method for nonseparable space–time generalized additive models
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https://doi.org/10.1007/s10182-017-0309-0 |
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Título
A penalized likelihood method for nonseparable space–time generalized additive modelsFecha de publicación
2018-07Editor
Springer VerlagISSN
1863-8171; 1863-818XCita bibliográfica
MOSAMMAM, Ali M.; MATEU, Jorge. A penalized likelihood method for nonseparable space–time generalized additive models. AStA Advances in Statistical Analysis, 2018, vol. 102, no 3, p. 333-357.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s10182-017-0309-0Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In this paper, we study space-time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve ... [+]
In this paper, we study space-time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the estimation of the response and smooth terms of GAMs. The results show that our space-time generalized additive models estimated response and smooth terms reasonable well, and in addition, the mean squared error, mean absolute deviation and coverage intervals improved considerably compared to the classic GAM. An application on particulate matter concentration in the North-Italian region of Piemonte is also presented. [-]
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AStA Advances in Statistical Analysis, 2018, vol. 102, no 3Derechos de acceso
© Springer Berlin Heidelberg
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