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pro vyhledávání: '"Groß Jürgen"'
Vine copulas are flexible dependence models using bivariate copulas as building blocks. If the parameters of the bivariate copulas in the vine copula depend on covariates, one obtains a conditional vine copula. We propose an extension for the estimat
Externí odkaz:
http://arxiv.org/abs/2406.13500
Autor:
Groß, Jürgen, Möller, Annette
In this note the use of the zero degree non-central chi squared distribution as predictive distribution for ensemble postprocessing is investigated. It has a point mass at zero by definition, and is thus particularly suited for postprocessing weather
Externí odkaz:
http://arxiv.org/abs/2404.04964
Nowadays, weather prediction is based on numerical weather prediction (NWP) models to produce an ensemble of forecasts. Despite of large improvements over the last few decades, they still tend to exhibit systematic bias and dispersion errors. Consequ
Externí odkaz:
http://arxiv.org/abs/2402.00555
Temporal, spatial or spatio-temporal probabilistic models are frequently used for weather forecasting. The D-vine (drawable vine) copula quantile regression (DVQR) is a powerful tool for this application field, as it can automatically select importan
Externí odkaz:
http://arxiv.org/abs/2309.05603
Autor:
Groß, Jürgen, Möller, Annette
The size of the effect of the difference in two groups with respect to a variable of interest may be estimated by the classical Cohen's $d$. A recently proposed generalized estimator allows conditioning on further independent variables within the fra
Externí odkaz:
http://arxiv.org/abs/2309.02069
Autor:
Groß, Jürgen, Möller, Annette
In this note, we reconsider Cohen's effect size measure $f^2$ under linear mixed models and demonstrate its application by employing an artificially generated data set. It is shown how $f^2$ can be computed with the statistical software environment R
Externí odkaz:
http://arxiv.org/abs/2302.14580
Autor:
Groß, Jürgen, Möller, Annette
In this note we introduce a generalized formula for Cohen's $d$ under the presence of additional independent variables, providing a measure for the size of a possible effect concerning the location difference of a variable in two groups. This is done
Externí odkaz:
http://arxiv.org/abs/2210.13048
Publikováno v:
International Journal of Research & Method, 2022
Methods based on machine learning become increasingly popular in many areas as they allow models to be fitted in a highly-data driven fashion, and often show comparable or even increased performance in comparison to classical methods. However, in the
Externí odkaz:
http://arxiv.org/abs/2210.11580