<div class="csl-bib-body">
<div class="csl-entry">Bura, E. (2023, December 18). <i>Fusing Sufficient Dimension Reduction with Neural Networks</i> [Conference Presentation]. 2023 IMS International Conference on Statistics and Data Science (ICSDS), Lisbon, Portugal.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192410
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dc.description
Neural networks are combined with sufficient dimension reduction methodology in order to remove the limitation of small p and n of the
latter. NN-SDR applies when the dependence of the response Y on a set of predictors X is fully captured by the regression function g(B′X),
for an unknown function g and low rank parameter B matrix. It is shown that the proposed estimator is on par with competing sufficient
dimension reduction methods, such as minimum average variance estimation and conditional variance estimation, in small p and n settings
in simulations. Its main advantage is its scalability in regressions with large data, for which the other methods are infeasible.
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dc.language.iso
en
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dc.subject
Large p and n
en
dc.subject
Regression
en
dc.subject
Mean subspace
en
dc.subject
Prediction
en
dc.subject
Nonparametric
en
dc.title
Fusing Sufficient Dimension Reduction with Neural Networks
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.type.category
Conference Presentation
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tuw.publication.invited
invited
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tuw.researchTopic.id
C6
-
tuw.researchTopic.id
A3
-
tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Fundamental Mathematics Research
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tuw.researchTopic.value
50
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tuw.researchTopic.value
50
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tuw.linking
https://www.icsds2023.com/quick-program
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tuw.publication.orgunit
E105-08 - Forschungsbereich Angewandte Statistik
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tuw.event.name
2023 IMS International Conference on Statistics and Data Science (ICSDS)
en
tuw.event.startdate
18-12-2023
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tuw.event.enddate
21-12-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Lisbon
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tuw.event.country
PT
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tuw.event.presenter
Bura, Efstathia
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
100
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item.languageiso639-1
en
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item.openairetype
conference paper not in proceedings
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item.grantfulltext
none
-
item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_18cp
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crisitem.author.dept
E105-08 - Forschungsbereich Angewandte Statistik
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crisitem.author.parentorg
E105 - Institut für Stochastik und Wirtschaftsmathematik
