A growing interest in non-Gaussian time series, particularly in series comprised of
nonnegative integers (counts), is taking place in today's statistics literature. Count series …

In recent years, copula models are being used in all areas of human endeavors including
energy, environment, social, natural and physical sciences. Copulas are the most powerful …

Count time series are widely encountered in practice. As with continuous valued data, many
count series have seasonal properties. This article uses a recent advance in stationary count …

This article develops the theory and methods for modeling a stationary count time series via
Gaussian transformations. The techniques use a latent Gaussian process and a …

This article explores models for geostatistical data for situations in which the region where
the phenomenon of interest varies is partitioned into two disjoint subregions. This is called a …

The class of bivariate integer-valued time series models, described via copula theory, is
gaining popularity in the literature because of applications in health sciences, engineering …

This work describes the R package gcKrig for the analysis of geostatistical count data using
Gaussian copulas. The package performs likelihood-based inference and spatial prediction …

Spatially referenced data arise in many fields, including imaging, ecology, public health, and
marketing. Although principled smoothing or interpolation is paramount for many …

This work investigates the computation of maximum likelihood estimators in Gaussian
copula models for geostatistical count data. This is a computationally challenging task …

The number and amount of claims, referred to as the sum of claims or the total claim/loss
amounts in insurance literature, are crucial pieces of information for insurance companies …