Tathagata Basu
Uncertainty Quantification in Lasso-Type Regularization Problems
Basu, Tathagata; Einbeck, Jochen; Troffaes, Matthias C.M.
Authors
Professor Jochen Einbeck jochen.einbeck@durham.ac.uk
Professor
Professor Matthias Troffaes matthias.troffaes@durham.ac.uk
Professor
Citation
Basu, T., Einbeck, J., & Troffaes, M. C. (2021). Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering (81-109). Springer Verlag. https://doi.org/10.1007/978-3-030-60166-9_3
Online Publication Date | Sep 10, 2020 |
---|---|
Publication Date | 2021 |
Deposit Date | Mar 9, 2021 |
Publicly Available Date | Sep 10, 2022 |
Publisher | Springer Verlag |
Pages | 81-109 |
Book Title | Optimization Under Uncertainty with Applications to Aerospace Engineering |
ISBN | 9783030601652 |
DOI | https://doi.org/10.1007/978-3-030-60166-9_3 |
Public URL | https://durham-repository.worktribe.com/output/1625807 |
Contract Date | Nov 13, 2018 |
Files
Accepted Book Chapter
(1.3 Mb)
PDF
Copyright Statement
This a post-peer-review, pre-copyedit version of a chapter published in Optimization Under Uncertainty with Applications to Aerospace Engineering. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-60166-9_3
You might also like
Robust uncertainty quantification for measurement problems with limited information
(2019)
Presentation / Conference Contribution
Bayesian Adaptive Selection Under Prior Ignorance
(-0001)
Presentation / Conference Contribution
A sensitivity analysis and error bounds for the adaptive lasso
(-0001)
Presentation / Conference Contribution
Binary Credal Classification Under Sparsity Constraints
(-0001)
Presentation / Conference Contribution
A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability
(-0001)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search