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pro vyhledávání: '"convex support"'
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss f
Externí odkaz:
http://arxiv.org/abs/2209.12538
Publikováno v:
In European Journal of Operational Research 16 March 2024 313(3):858-870
We aim to improve upon the exploration of the general-purpose random walk Metropolis algorithm when the target has non-convex support $A \subset \mathbb{R}^d$, by reusing proposals in $A^c$ which would otherwise be rejected. The algorithm is Metropol
Externí odkaz:
http://arxiv.org/abs/1905.09964
Publikováno v:
In Knowledge-Based Systems 27 September 2021 228
Autor:
Weis, Stephan
Publikováno v:
Linear Algebra and its Applications 435 3168-3188 (2011); correction: ibid 436 xvi (2012)
Convex support, the mean values of a set of random variables, is central in information theory and statistics. Equally central in quantum information theory are mean values of a set of observables in a finite-dimensional C*-algebra A, which we call (
Externí odkaz:
http://arxiv.org/abs/1101.3098
Akademický článek
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Akademický článek
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Publikováno v:
European Journal of Operational Research.
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e255a2e7937e2e39802b0086e92035f9
https://doi.org/10.1016/j.ejor.2023.05.009
https://doi.org/10.1016/j.ejor.2023.05.009
Publikováno v:
Moriarty, J, Vogrinc, J & Zocca, A 2021, ' A Metropolis-class sampler for targets with non-convex support ', Statistics and Computing, vol. 31, no. 6, 72 . https://doi.org/10.1007/s11222-021-10044-4
Statistics and Computing, 31(6):72. Springer Netherlands
Statistics and Computing, 31(6):72. Springer Netherlands
We aim to improve upon the exploration of the general-purpose random walk Metropolis algorithm when the target has non-convex support $A \subset \mathbb{R}^d$, by reusing proposals in $A^c$ which would otherwise be rejected. The algorithm is Metropol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8aa0b824ddf805293fc5f8f6b242252
https://doi.org/10.1007/s11222-021-10044-4
https://doi.org/10.1007/s11222-021-10044-4