| | |
| | |
Stat |
Members: 3667 Articles: 2'599'751 Articles rated: 2609
15 February 2025 |
|
| | | |
|
Article overview
| |
|
A Distributionally Robust Optimization Framework for Extreme Event Estimation | Yuanlu Bai
; Henry Lam
; Xinyu Zhang
; | Date: |
3 Jan 2023 | Abstract: | Conventional methods for extreme event estimation rely on well-chosen
parametric models asymptotically justified from extreme value theory (EVT).
These methods, while powerful and theoretically grounded, could however
encounter a difficult bias-variance tradeoff that exacerbates especially when
data size is too small, deteriorating the reliability of the tail estimation.
In this paper, we study a framework based on the recently surging literature of
distributionally robust optimization. This approach can be viewed as a
nonparametric alternative to conventional EVT, by imposing general shape belief
on the tail instead of parametric assumption and using worst-case optimization
as a resolution to handle the nonparametric uncertainty. We explain how this
approach bypasses the bias-variance tradeoff in EVT. On the other hand, we face
a conservativeness-variance tradeoff which we describe how to tackle. We also
demonstrate computational tools for the involved optimization problems and
compare our performance with conventional EVT across a range of numerical
examples. | Source: | arXiv, 2301.01360 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|