Kernel-based functional principal components
In this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions. © 2000 Elsevier Science B.V.
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| Lenguaje: | English |
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01677152_v48_n4_p335_Boente |
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todo:paper_01677152_v48_n4_p335_Boente2023-10-03T15:05:13Z Kernel-based functional principal components Boente, G. Fraiman, R. 62H25 Eigenfunctions Functional principal components Kernel methods* Hilbert-Schmidt operators Primary 62G07 In this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions. © 2000 Elsevier Science B.V. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR English info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01677152_v48_n4_p335_Boente |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| language |
English |
| orig_language_str_mv |
English |
| topic |
62H25 Eigenfunctions Functional principal components Kernel methods* Hilbert-Schmidt operators Primary 62G07 |
| spellingShingle |
62H25 Eigenfunctions Functional principal components Kernel methods* Hilbert-Schmidt operators Primary 62G07 Boente, G. Fraiman, R. Kernel-based functional principal components |
| topic_facet |
62H25 Eigenfunctions Functional principal components Kernel methods* Hilbert-Schmidt operators Primary 62G07 |
| description |
In this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions. © 2000 Elsevier Science B.V. |
| format |
JOUR |
| author |
Boente, G. Fraiman, R. |
| author_facet |
Boente, G. Fraiman, R. |
| author_sort |
Boente, G. |
| title |
Kernel-based functional principal components |
| title_short |
Kernel-based functional principal components |
| title_full |
Kernel-based functional principal components |
| title_fullStr |
Kernel-based functional principal components |
| title_full_unstemmed |
Kernel-based functional principal components |
| title_sort |
kernel-based functional principal components |
| url |
http://hdl.handle.net/20.500.12110/paper_01677152_v48_n4_p335_Boente |
| work_keys_str_mv |
AT boenteg kernelbasedfunctionalprincipalcomponents AT fraimanr kernelbasedfunctionalprincipalcomponents |
| _version_ |
1782029735904149504 |