The effect of explanatory environmental variables on a species' distribution is often
assessed using a count regression model. Poisson generalized linear models or negative …

The main goal of disease mapping is to estimate disease risk and identify high-risk areas.
Such analyses are hampered by the limited geographical resolution of the available data …

Applying quantile regression to count data presents logical and practical complications
which are usually solved by artificially smoothing the discrete response variable through …

Due to the significant influence of climate change and human activities on the water cycle,
accurately estimating short-and long-term water availability has become imperative. This …

The integrated nested Laplace approximation (INLA) is a deterministic approach to
Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate …

<? show [AQ ID= GQ2 POS=-20pt]?><? show [AQ ID= GQ5 POS= 12pt]?> Patient-reported
outcomes (PROs) that aim to measure patients' subjective attitudes towards their health or …

Although ubiquitous in the sciences, histogram data have not received much attention by the
Deep Learning community. Whilst regression and classification tasks for scalar and vector …

Binary regression models are ubiquitous in virtually every scientific field. Frequently,
traditional generalised linear models fail to capture the variability in the probability surface …

Although quantile regression has emerged as a powerful tool for understanding various
quantiles of a response variable conditioned on a set of covariates, the development of …

The identification and estimation of conditional quantile functions for count responses using
longitudinal data are considered. The approach is based on a continuous approximation to …