Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
01.07.2009
Verlag
VDMSeitenzahl
112
Maße (L/B/H)
21,9/14,9/1 cm
Gewicht
160 g
Sprache
Englisch
ISBN
978-3-639-17792-3
The basic philosophy of Functional Data Analysis
(FDA) is to think of the observed data functions as
elements of a possibly infinite-dimensional function
space. Most of the current research topics on FDA
focus on advancing theoretical tools and extending
existing multivariate techniques to accommodate the
infinite-dimensional nature of data. This monograph
reports contributions on both fronts, where a
unifying inverse regression theory for both the
multivariate setting and functional data from a
Reproducing Kernel Hilbert Space (RKHS) prospective
is developed.
We proposed a stochastic multiple-index model, two
RKHS-related inverse regression procedures, a
``slicing'' approach and a kernel approach, as well
as an asymptotic theory were introduced to the
statistical framework. Some general computational
issues of FDA were discussed, Some
general computational issues of FDA were discussed,
which led to smoothed versions of the stochastic
inverse regression methods.
(FDA) is to think of the observed data functions as
elements of a possibly infinite-dimensional function
space. Most of the current research topics on FDA
focus on advancing theoretical tools and extending
existing multivariate techniques to accommodate the
infinite-dimensional nature of data. This monograph
reports contributions on both fronts, where a
unifying inverse regression theory for both the
multivariate setting and functional data from a
Reproducing Kernel Hilbert Space (RKHS) prospective
is developed.
We proposed a stochastic multiple-index model, two
RKHS-related inverse regression procedures, a
``slicing'' approach and a kernel approach, as well
as an asymptotic theory were introduced to the
statistical framework. Some general computational
issues of FDA were discussed, Some
general computational issues of FDA were discussed,
which led to smoothed versions of the stochastic
inverse regression methods.
Noch keine Bewertungen vorhanden
Verfassen Sie die erste Bewertung zu diesem Artikel
Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.
Kurze Frage zu unserer Seite
Vielen Dank für dein Feedback
Wir nutzen dein Feedback, um unsere Produktseiten zu verbessern. Bitte habe Verständnis, dass wir dir keine Rückmeldung geben können. Falls du Kontakt mit uns aufnehmen möchtest, kannst du dich aber gerne an unseren Kund*innenservice wenden.
zum Kundenservice