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...
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Gespeichert in:

Autor*in:	Salmerón, R. [verfasserIn] Ruiz-Medina, M. D. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Functional data analysis Functional forecasting -Dimensional singular value decomposition

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 23(2008), 3 vom: 05. Feb., Seite 289-297
Übergeordnetes Werk:	volume:23 ; year:2008 ; number:3 ; day:05 ; month:02 ; pages:289-297

Links:	Volltext

DOI / URN:	10.1007/s00477-008-0213-y

Katalog-ID:	SPR006398707

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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.
650		4	\|a Functional data analysis \|7 (dpeaa)DE-He213
650		4	\|a Functional forecasting \|7 (dpeaa)DE-He213
650		4	\|a -Dimensional singular value decomposition \|7 (dpeaa)DE-He213
700	1		\|a Ruiz-Medina, M. D. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Stochastic environmental research and risk assessment \|d Berlin : Springer, 1987 \|g 23(2008), 3 vom: 05. Feb., Seite 289-297 \|w (DE-627)27160235X \|w (DE-600)1481263-0 \|x 1436-3259 \|7 nnns
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912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4277
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
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author_variant	r s rs m d r m mdr mdrm
matchkey_str	article:14363259:2008----::utsetadcmoiinfucinluo
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bklnumber	43.03 58.50
publishDate	2008
allfields	10.1007/s00477-008-0213-y doi (DE-627)SPR006398707 (SPR)s00477-008-0213-y-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213 Ruiz-Medina, M. D. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://dx.doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 23 2008 3 05 02 289-297
spelling	10.1007/s00477-008-0213-y doi (DE-627)SPR006398707 (SPR)s00477-008-0213-y-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213 Ruiz-Medina, M. D. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://dx.doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 23 2008 3 05 02 289-297
allfields_unstemmed	10.1007/s00477-008-0213-y doi (DE-627)SPR006398707 (SPR)s00477-008-0213-y-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213 Ruiz-Medina, M. D. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://dx.doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 23 2008 3 05 02 289-297
allfieldsGer	10.1007/s00477-008-0213-y doi (DE-627)SPR006398707 (SPR)s00477-008-0213-y-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213 Ruiz-Medina, M. D. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://dx.doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 23 2008 3 05 02 289-297
allfieldsSound	10.1007/s00477-008-0213-y doi (DE-627)SPR006398707 (SPR)s00477-008-0213-y-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Salmerón, R. verfasserin aut Multi-spectral decomposition of functional autoregressive models 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213 Ruiz-Medina, M. D. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 23(2008), 3 vom: 05. Feb., Seite 289-297 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:23 year:2008 number:3 day:05 month:02 pages:289-297 https://dx.doi.org/10.1007/s00477-008-0213-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 23 2008 3 05 02 289-297
language	English
source	Enthalten in Stochastic environmental research and risk assessment 23(2008), 3 vom: 05. Feb., Seite 289-297 volume:23 year:2008 number:3 day:05 month:02 pages:289-297
sourceStr	Enthalten in Stochastic environmental research and risk assessment 23(2008), 3 vom: 05. Feb., Seite 289-297 volume:23 year:2008 number:3 day:05 month:02 pages:289-297
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Functional data analysis Functional forecasting -Dimensional singular value decomposition
dewey-raw	550
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Salmerón, R. @@aut@@ Ruiz-Medina, M. D. @@aut@@
publishDateDaySort_date	2008-02-05T00:00:00Z
hierarchy_top_id	27160235X
dewey-sort	3550
id	SPR006398707
language_de	englisch
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author	Salmerón, R.
spellingShingle	Salmerón, R. ddc 550 bkl 43.03 bkl 58.50 misc Functional data analysis misc Functional forecasting misc -Dimensional singular value decomposition Multi-spectral decomposition of functional autoregressive models
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topic_title	550 ASE 43.03 bkl 58.50 bkl Multi-spectral decomposition of functional autoregressive models Functional data analysis (dpeaa)DE-He213 Functional forecasting (dpeaa)DE-He213 -Dimensional singular value decomposition (dpeaa)DE-He213
topic	ddc 550 bkl 43.03 bkl 58.50 misc Functional data analysis misc Functional forecasting misc -Dimensional singular value decomposition
topic_unstemmed	ddc 550 bkl 43.03 bkl 58.50 misc Functional data analysis misc Functional forecasting misc -Dimensional singular value decomposition
topic_browse	ddc 550 bkl 43.03 bkl 58.50 misc Functional data analysis misc Functional forecasting misc -Dimensional singular value decomposition
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title	Multi-spectral decomposition of functional autoregressive models
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title_full	Multi-spectral decomposition of functional autoregressive models
author_sort	Salmerón, R.
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author_browse	Salmerón, R. Ruiz-Medina, M. D.
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title_sort	multi-spectral decomposition of functional autoregressive models
title_auth	Multi-spectral decomposition of functional autoregressive models
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Multi-spectral decomposition of functional autoregressive models
url	https://dx.doi.org/10.1007/s00477-008-0213-y
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author2	Ruiz-Medina, M. D.
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up_date	2024-07-03T22:49:01.209Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006398707</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110190411.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2008 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00477-008-0213-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006398707</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00477-008-0213-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Salmerón, R.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-spectral decomposition of functional autoregressive models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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Multi-spectral decomposition of functional autoregressive models

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