zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Multilayer perceptron with functional inputs: an inverse regression approach. (English) Zbl 1164.62339
A functional sliced inverse regression (FSIR) technique is considered for fitting the model $$ Y=f(\langle X,a_1\rangle,\dots,\langle X,a_p\rangle,\varepsilon), $$ where $Y$ is the response, $X$ is a functional regressor (a random function in $L_2[a,b]$), $f$ is an unknown regression function, $a_1$ are unknown functions, and $\varepsilon$ is an error term. Consistency of regularized FSIR estimates for $a_i$ is demonstrated. It is proposed to estimate the function $f$ by a multilayer perceptron technique. Consistency of the proposed training algorithm for such perceptrons is shown. Applications to real data are considered.
Reviewer: R. E. Maiboroda (Kyïv)

62G08Nonparametric regression
62G20Nonparametric asymptotic efficiency
62G99Nonparametric inference
62J12Generalized linear models
fda (R)
Full Text: DOI arXiv