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pro vyhledávání: '"Rohrbeck, Christian"'
Regression analysis under the assumption of monotonicity is a well-studied statistical problem and has been used in a wide range of applications. However, there remains a lack of a broadly applicable methodology that permits information borrowing, fo
Externí odkaz:
http://arxiv.org/abs/2305.17711
Autor:
Rohrbeck, Christian, Tawn, Jonathan A
A key aspect where extreme values methods differ from standard statistical models is through having asymptotic theory to provide a theoretical justification for the nature of the models used for extrapolation. In multivariate extremes many different
Externí odkaz:
http://arxiv.org/abs/2209.10936
Autor:
Rohrbeck, Christian, Cooley, Daniel
Publikováno v:
Annals of Applied Statistics 17(2): 1333-1352, June 2023
Hazard event sets, a collection of synthetic extreme events over a given period, are important for catastrophe modelling. This paper addresses the issue of generating event sets of extreme river flow for northern England and southern Scotland, a regi
Externí odkaz:
http://arxiv.org/abs/2106.00630
Publikováno v:
Bayesian Analysis. 2023 Mar; 18(1): 193-221
Compared to the nominal scale, the ordinal scale for a categorical outcome variable has the property of making a monotonicity assumption for the covariate effects meaningful. This assumption is encoded in the commonly used proportional odds model, bu
Externí odkaz:
http://arxiv.org/abs/2007.01390
Autor:
Rohrbeck, Christian, Tawn, Jonathan A
To address the need for efficient inference for a range of hydrological extreme value problems, spatial pooling of information is the standard approach for marginal tail estimation. We propose the first extreme value spatial clustering methods which
Externí odkaz:
http://arxiv.org/abs/1906.08522
Autor:
Rohrbeck, Christian, Tawn, Jonathan A.
Publikováno v:
Stat; Dec2024, Vol. 13 Issue 1, p1-13, 13p
We consider monotonic, multiple regression for a set of contiguous regions (lattice data). The regression functions permissibly vary between regions and exhibit geographical structure. We develop new Bayesian non-parametric methodology which allows f
Externí odkaz:
http://arxiv.org/abs/1605.06025
Publikováno v:
The Annals of Applied Statistics, 2018 Mar 01. 12(1), 246-282.
Externí odkaz:
https://www.jstor.org/stable/26542528
Publikováno v:
Saarela, Olli Rohrbeck, Christian Arjas, Elja . Bayesian Non-Parametric Ordinal Regression Under a Monotonicity Constraint*. Bayesian Analysis. 2023, 18(1), 193-221
Bayesian Analysis
Bayesian Analysis
Externí odkaz:
http://hdl.handle.net/10852/109613
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