- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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A deep learning approach to censored regression ; volume:27 ; number:1 ; day:28 ; month:2 ; year:2024 ; pages:1-37 ; date:3.2024
Pattern analysis and applications ; 27, Heft 1 (28.2.2024), 1-37, 3.2024
- Creator
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Dănăilă, Vlad-Rareş
Buiu, Cătălin
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s10044-024-01216-9
- URN
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urn:nbn:de:101:1-2405152137565.500146234819
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:50 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Dănăilă, Vlad-Rareş
- Buiu, Cătălin
- SpringerLink (Online service)
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Censored quantile regression processes under dependence and penalization
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AN ADDITIVE RISKS REGRESSION MODEL FOR MIDDLE-CENSORED LIFETIME DATA
Scale checks in censored regression
Bias-corrected quantile regression estimation of censored regression models
Nonparametric quantile regression for twice censored data
Type I Tobit Bayesian Additive Regression Trees for censored outcome regression
The effects of works councils on overtime hours - a censored quantile regression approach
Local polynomial regression with truncated or censored response
Geoadditive hazard regression for interval censored survival times
Censored quantile regression processes under dependence and penalization
Deep Learning for Geospatial Environmental Regression
Censored quantile regression survival models with a cure proportion
Smoothing spline regression estimates for randomly right censored data