Quantitative bounds for large deviations of heavy tailed random variables
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911776624345088 |
|---|---|
| author | Vogel, Quirin |
| author_facet | Vogel, Quirin |
| contents | The probability that the sum of independent, centered, identically distributed, heavy-tailed random variables achieves a very large value is asymptotically equal to the probability that there exists a single summand equalling that value. We quantify the error in this approximation. We furthermore characterise of the law of the individual summands, conditioned on the sum being large. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_02935 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Quantitative bounds for large deviations of heavy tailed random variables Vogel, Quirin Probability 60F10 (Primary), 60B10 (Secondary) The probability that the sum of independent, centered, identically distributed, heavy-tailed random variables achieves a very large value is asymptotically equal to the probability that there exists a single summand equalling that value. We quantify the error in this approximation. We furthermore characterise of the law of the individual summands, conditioned on the sum being large. |
| title | Quantitative bounds for large deviations of heavy tailed random variables |
| topic | Probability 60F10 (Primary), 60B10 (Secondary) |
| url | https://arxiv.org/abs/2202.02935 |