Flexible modeling of how an entire distribution changes with covariates is an important yet
challenging generalization of mean-based regression that has seen growing interest over …

Distributional regression models that overcome the traditional focus on relating the
conditional mean of the response to explanatory variables and instead target either the …

Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …

The goal of quantile regression is to estimate conditional quantiles for specified values of
quantile probability using linear or nonlinear regression equations. These estimates are …

In the practice of point prediction, it is desirable that forecasters receive a directive in the
form of a statistical functional. For example, forecasters might be asked to report the mean or …

We use tail expectiles to estimate alternative measures to the value at risk and marginal
expected shortfall, which are two instruments of risk protection of utmost importance in …

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 …

Isotonic distributional regression (IDR) is a powerful non-parametric technique for the
estimation of conditional distributions under order restrictions. In a nutshell, IDR learns …

In this paper, the problem of the nonparametric estimation of the expectile regression model
for strong mixing functional time series data is investigated. To be more precise, we …

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement
statistical models for ecological and environmental data analysis. We open the black box …