Vine copula based modeling
With the availability of massive multivariate data comes a need to develop flexible
multivariate distribution classes. The copula approach allows marginal models to be …
multivariate distribution classes. The copula approach allows marginal models to be …
A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
D-vine copula based quantile regression
Quantile regression, that is the prediction of conditional quantiles, has steadily gained
importance in statistical modeling and financial applications. A new semiparametric quantile …
importance in statistical modeling and financial applications. A new semiparametric quantile …
A review of probabilistic forecasting and prediction with machine learning
H Tyralis, G Papacharalampous - arXiv preprint arXiv:2209.08307, 2022 - arxiv.org
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
Examination and visualisation of the simplifying assumption for vine copulas in three dimensions
Vine copulas are a highly flexible class of dependence models, which are based on the
decomposition of the density into bivariate building blocks. For applications one usually …
decomposition of the density into bivariate building blocks. For applications one usually …
Copula modeling from Abe Sklar to the present day
Copula modeling from Abe Sklar to the present day - ScienceDirect Skip to main contentSkip
to article Elsevier logo Journals & Books Search RegisterSign in View PDF Download full …
to article Elsevier logo Journals & Books Search RegisterSign in View PDF Download full …
Nonparametric C-and D-vine-based quantile regression
M Tepegjozova, J Zhou, G Claeskens… - Dependence …, 2022 - degruyter.com
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 quantile …
complementary method to linear regression, since computing a range of conditional quantile …
Neural networks for partially linear quantile regression
Deep learning has enjoyed tremendous success in a variety of applications but its
application to quantile regression remains scarce. A major advantage of the deep learning …
application to quantile regression remains scarce. A major advantage of the deep learning …
Accounting for endogeneity in regression models using Copulas: A step-by-step guide for empirical studies
A Papadopoulos - Journal of Econometric Methods, 2022 - degruyter.com
We provide a detailed presentation and guide for the use of Copulas in order to account for
endogeneity in linear regression models without the need for instrumental variables. We …
endogeneity in linear regression models without the need for instrumental variables. We …
Copula-based quantile regression for longitudinal data
Inference and prediction in quantile regression for longitudinal data are challenging without
parametric distributional assumptions. We propose a new semiparametric approach that …
parametric distributional assumptions. We propose a new semiparametric approach that …