Modeling Multiple Risks: Hidden Domain of Attraction
Abhimanyu Mitra, Sidney I. Resnick
TL;DR
This paper introduces the hidden domain of attraction, a generalization of hidden regular variation, to improve the modeling and estimation of joint tail probabilities in multivariate extreme value analysis.
Contribution
It extends the concept of hidden regular variation to a broader framework called hidden domain of attraction, with examples and techniques for detection and estimation.
Findings
The new model captures more complex tail dependencies.
Examples demonstrate the necessity of the generalized model.
Detection and estimation methods are discussed.
Abstract
Hidden regular variation is a sub-model of multivariate regular variation and facilitates accurate estimation of joint tail probabilities. We generalize the model of hidden regular variation to what we call hidden domain of attraction. We exhibit examples that illustrate the need for a more general model and discuss detection and estimation techniques.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Statistical Distribution Estimation and Applications