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 …

This book is written for graduate students and researchers, who are interested in using
copula-based models for multivariate data structures. It provides a step-by-step introduction …

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 …

In this work, a novel technique for efficient computation of bivariate empirical copulas and,
by extension, non-parametrical copulas. The algorithm addresses discrete and finite …

The existing literature of copula-based regression assumes that complete data are
available, but this assumption is violated in many real applications. The present paper …

Composite quantile regression (CQR) is becoming increasingly popular due to its
robustness from quantile regression. Recently, the CQR method has been studied …

In this paper, we consider non-stationary random vectors, where the marginal distributions
and the associated copula may be time-dependent. We propose estimators for the unknown …

Simulation-optimization techniques are effective in identifying an optimal remediation
strategy. Simulation models with uncertainty, primarily in the form of parameter uncertainty …

The paper proposes a new class of link functions for generalized binary regression based
on copula models. The idea consists of writing the predictive probability of success (PPOS) …

Thanks to their ability to capture complex dependence structures, copulas are frequently
used to glue random variables into a joint model with arbitrary marginal distributions. More …