Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Konzen, Evandro"'
Autor:
Konzen, Evandro1,2 (AUTHOR), Delahay, Richard J.3,4 (AUTHOR), Hodgson, Dave J.5 (AUTHOR), McDonald, Robbie A.4,5 (AUTHOR), Brooks Pollock, Ellen6 (AUTHOR), Spencer, Simon E. F.2 (AUTHOR), McKinley, Trevelyan J.1 (AUTHOR) t.mckinley@exeter.ac.uk
Publikováno v:
PLoS Computational Biology. 11/19/2024, Vol. 20 Issue 11, p1-20. 20p.
We present and describe the GPFDA package for R. The package provides flexible functionalities for dealing with Gaussian process regression (GPR) models for functional data. Multivariate functional data, functional data with multidimensional inputs,
Externí odkaz:
http://arxiv.org/abs/2102.00249
Models for extreme values accommodating non-stationarity have been amply studied and evaluated from a parametric perspective. Whilst these models are flexible, in the sense that many parametrizations can be explored, they assume an asymptotic distrib
Externí odkaz:
http://arxiv.org/abs/2005.13490
We discuss a general Bayesian framework on modeling multidimensional function-valued processes by using a Gaussian process or a heavy-tailed process as a prior, enabling us to handle nonseparable and/or nonstationary covariance structure. The nonstat
Externí odkaz:
http://arxiv.org/abs/1903.09981
Autor:
Konzen, Evandro
Publikováno v:
Biblioteca Digital de Teses e Dissertações da UFRGSUniversidade Federal do Rio Grande do SulUFRGS.
Este trabalho aplica algumas formas de penalização tipo LASSO aos coeficientes para reduzir a dimensionalidade do espaço paramétrico em séries temporais, no intuito de melhorar as previsões fora da amostra. Particularmente, o método denominado
Externí odkaz:
http://hdl.handle.net/10183/103896
Modeling nonstationary extremes of storm severity: comparing parametric and semiparametric inference
This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::1b60cd055287e8dc54fbe1202c2e4fe7
https://centaur.reading.ac.uk/95821/1/env.2667.pdf
https://centaur.reading.ac.uk/95821/1/env.2667.pdf
Autor:
Konzen, Evandro1, Ziegelmann, Flavio A.2 flavioaz@mat.ufrgs.br
Publikováno v:
Journal of Forecasting. Nov2016, Vol. 35 Issue 7, p592-612. 21p.