1 citations · 2 across the 3 of their papers we have counts for
3 papers
stat.AP2023★ 1 cited
Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria
Marija Tepegjozova, Benjamin F. Meyer, Anja Rammig +2
In light of climate change's impacts on forests, including extreme drought and late-frost, leading to vitality decline and regional forest die-back, we assess univariate drought an…
stat.ME2022
Bivariate vine copula based regression, bivariate level and quantile curves
Marija Tepegjozova, Claudia Czado
The statistical analysis of univariate quantiles is a well developed research topic. However, there is a need for research in multivariate quantiles. We construct bivariate (condit…
stat.ME2021★ 1 cited
Nonparametric C- and D-vine based quantile regression
Marija Tepegjozova, Jing Zhou, Gerda Claeskens +1
Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional q…