Inference under functional proportional and common principal component models

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In many situations, when dealing with several populations with different covariance operators, equality of the operators is assumed. Usually, if this assumption does not hold, one estimates the covariance operator of each group separately, which leads to a large number of parameters. As in the multi...

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Detalles Bibliográficos
Autores principales: Boente, G., Rodriguez, D., Sued, M.
Formato: JOUR
Materias:
Common principal components
Eigenfunctions
Functional data analysis
Hilbert-Schmidt operators
Kernel methods
Proportional model
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_0047259X_v101_n2_p464_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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