Implicit copulas are the most common copula choice for modeling dependence in high
dimensions. This broad class of copulas is introduced and surveyed, including elliptical …

This article describes the R package gcmr for fitting Gaussian copula marginal regression
models. The Gaussian copula provides a mathematically convenient framework to handle …

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 …

We propose a new model for regression and dependence analysis when addressing spatial
data with possibly heavy tails and an asymmetric marginal distribution. We first propose a …

Identifying spatial clusters of different regression coefficients is a useful tool for discerning
the distinctive relationship between a response and covariates in space. Most of the existing …

This paper presents a unified framework for supervised learning and inference procedures
using the divide-and-conquer approach for high-dimensional correlated outcomes. We …

A highly scalable method for (non-) Gaussian random fields estimation is proposed. In
particular, a novel (a) symmetric weight function based on nearest neighbors for the method …

Inference and prediction in quantile regression for longitudinal data are challenging without
parametric distributional assumptions. We propose a new semiparametric approach that …

This article gives a narrative overview of what constitutes climatological data and their
typical features, with a focus on aspects relevant to statistical modeling. We restrict the …

Learning the customers' experience and behavior creates competitive advantages for any
company over its rivals. The insurance industry is an essential sector in any developed …