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 …

The global sensitivity analysis method used to quantify the influence of uncertain input
variables on the variability in numerical model responses has already been applied to …

Generalized linear models are a widely used method to obtain parametric estimates for the
mean function. They have been further extended to allow the relationship between the mean …

Medical cost data are often skewed to the right and heteroscedastic, having a nonlinear
relation with covariates. To tackle these issues, we consider an extension to generalized …

Atmospheric thermodynamic data are gathered by high technology remote instruments such
as radiosondes, giving rise to profiles that are usually modelled as functions depending only …

When describing adequately complex data structures one is often confronted with the fact
that mean as well as variance (or more generally dispersion) is highly influenced by some …

Widely used methods such as Cox proportional hazards, accelerated failure time, and
Bennet proportional odds models do not model the quantiles directly, but rather allow to …

Monitoring plant and animal populations is an important goal for both academic research
and management of natural resources. Successful management of populations often …

The study of how the number of spikes in a middle temporal visual area (MT/V5) neuron is
tuned to the direction of a visual stimulus has attracted considerable attention over the years …

Even though dropout is a popular regularization technique, its theoretical properties are not
fully understood. In this paper we study dropout regularization in extended generalized …