The classical modeling of spatial extremes relies on asymptotic models (ie, max‐stable or r‐
Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at …

Accurate time-series forecasting during high variance segments (eg, holidays), is critical for
anomaly detection, optimal resource allocation, budget planning and other related tasks. At …

Reliable uncertainty estimation for time series prediction is critical in many fields, including
physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for …

Many environmental processes exhibit weakening spatial dependence as events become
more extreme. Well-known limiting models, such as max-stable or generalized Pareto …

Gaussian scale mixtures are constructed as Gaussian processes with a random variance.
They have non-Gaussian marginals and can exhibit asymptotic dependence unlike …

The health consequences of climate variability and change are diverse, potentially affecting
the burden of a wide range of health outcomes, including illnesses and deaths related to …

We propose a new copula model that can be used with replicated spatial data. Unlike the
multivariate normal copula, the proposed copula is based on the assumption that a common …

Many modern software systems and applications are built using heterogeneous services
provided by a range of devices, from high-power devices located in the Cloud to potentially …

To disentangle the complex nonstationary dependence structure of precipitation extremes
over the entire contiguous United States (US), we propose a flexible local approach based …

Capturing the potentially strong dependence among the peak concentrations of multiple air
pollutants across a spatial region is crucial for assessing the related public health risks. In …