Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

With the availability of massive multivariate data comes a need to develop flexible
multivariate distribution classes. The copula approach allows marginal models to be …

Technological advances have enabled a new class of multivariate models for ecology, with
the potential now to specify a statistical model for abundances jointly across many taxa, to …

Dependence Modeling with Copulas covers the substantial advances that have taken place
in the field during the last 15 years, including vine copula modeling of high-dimensional …

We present a regression technique for data-driven problems based on polynomial chaos
expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ) …

The Poisson distribution has been widely studied and used for modeling univariate count‐
valued data. However, multivariate generalizations of the Poisson distribution that permit …

The rise of new media is hel** marketers evolve from digital to interactive marketing,
which facilitates a two-way communication between marketers and customers without …

In insurance and related industries including healthcare, it is common to have several
outcome measures that the analyst wishes to understand using explanatory variables. For …

Abstract Systems subject to uncertain inputs produce uncertain responses. Uncertainty
quantification (UQ) deals with the estimation of statistics of the system response, given a …

Vine copula models have proven themselves as a very flexible class of multivariate copula
models with regard to symmetry and tail dependence for pairs of variables. The full …