Optimal design of sea-walls requires the extreme value analysis of a variety of oceanographic data. Asymptotic arguments suggest the use of multivariate extreme value models, but empirical studies ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in ...

In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both ...

TSD 20: Multivariate meta-analysis of summary data for combining treatment effects on correlated outcomes and evaluating surrogate endpoints (PDF, 1.2MB) – October 2019 – Updated December 2022: ...

Data clustering is the process of grouping data items so that similar items are placed in the same cluster. There are several different clustering techniques, and each technique has many variations.

This paper presents a discrete random-field model for forward prices driven by the multivariate normal inverse Gaussian distribution. The model captures the idiosyncratic risk and adequately addresses ...