Multi-spectral decomposition of functional autoregressive models
Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space contex...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Salmerón, R. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Stochastic environmental research and risk assessment - Springer-Verlag, 1999, 23(2008), 3 vom: 05. Feb., Seite 289-297 |
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| Übergeordnetes Werk: |
volume:23 ; year:2008 ; number:3 ; day:05 ; month:02 ; pages:289-297 |
| Links: |
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| DOI / URN: |
10.1007/s00477-008-0213-y |
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OLC2058732200 |
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| 520 | |a Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. | ||
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| 650 | 4 | |a Functional forecasting | |
| 650 | 4 | |a -Dimensional singular value decomposition | |
| 700 | 1 | |a Ruiz-Medina, M. D. |4 aut | |
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10.1007/s00477-008-0213-y doi (DE-627)OLC2058732200 (DE-He213)s00477-008-0213-y-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2008 Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. Functional data analysis Functional forecasting -Dimensional singular value decomposition Ruiz-Medina, M. D. aut Enthalten in Stochastic environmental research and risk assessment Springer-Verlag, 1999 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4700 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 23 2008 3 05 02 289-297 |
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10.1007/s00477-008-0213-y doi (DE-627)OLC2058732200 (DE-He213)s00477-008-0213-y-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2008 Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. Functional data analysis Functional forecasting -Dimensional singular value decomposition Ruiz-Medina, M. D. aut Enthalten in Stochastic environmental research and risk assessment Springer-Verlag, 1999 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4700 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 23 2008 3 05 02 289-297 |
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10.1007/s00477-008-0213-y doi (DE-627)OLC2058732200 (DE-He213)s00477-008-0213-y-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2008 Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. Functional data analysis Functional forecasting -Dimensional singular value decomposition Ruiz-Medina, M. D. aut Enthalten in Stochastic environmental research and risk assessment Springer-Verlag, 1999 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4700 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 23 2008 3 05 02 289-297 |
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Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. © Springer-Verlag 2008 |
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Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. © Springer-Verlag 2008 |
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Abstract Functional data models provides a suitable framework for the statistical analysis of several environmental phenomena involving continuous time evolution and/or spatial variation. The functional autoregressive model of order p, p ≥ 1, (FAR(p)) extends to the infinite-dimensional space context the classical autoregressive model AR(p) (see, for example, Mourid T (1993) Processus autorégressiifs d’ordre supérieur. Acad Sci t.317(Sér. I):1167–1172). In this paper, we derive a multidimensional diagonalization of the functional parameters (operators) involved in the FAR(p), p > 1, formulation. The functional state equation is then transformed into a discrete system of scalar state equations. The decomposition obtained is optimal regarding information on spatiotemporal interaction affecting the evolution of the spatial behavior of the process of interest. For functional prediction and filtering, we implement the Kalman filter equations from the diagonal version derived for FAR(p) models. © Springer-Verlag 2008 |
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