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Statistics of multivariate extremes under random censoring — arXiv:2609.37904

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37904
License (as archived): CC0-1.0
Archived: 2026-10-07T18:00:58.719Z
SHA-256 of the archived text: b4efe6f3ec709312f70ee8545db74d9da9e43f3a8da7147adb20ecf524106a91
Stable URL of this page: https://metraipolis.com/documento/dc_4610cd5ee855

How to cite

Statistics of multivariate extremes under random censoring — arXiv:2609.37904 — arXiv (en), https://arxiv.org/abs/2609.37904 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4610cd5ee855 (Metraipolis archive dc_4610cd5ee855, sha256 b4efe6f3ec709312f70ee8545db74d9da9e43f3a8da7147adb20ecf524106a91)

BibTeX

@misc{dc4610cd5ee855,
  title = {Statistics of multivariate extremes under random censoring — arXiv:2609.37904},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37904},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4610cd5ee855; sha256 b4efe6f3ec709312f70ee8545db74d9da9e43f3a8da7147adb20ecf524106a91},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37904. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study tail dependence of a $d$-dimensional random vector whose coordinates are subject to random right censoring. Along each fixed direction the censored problem reduces exactly to a univariate one, and the observed data determine the radius and whether it was produced by the event or censoring vector. An ordinary Kaplan--Meier product limit therefore estimates the joint tail probability in that direction, in every dimension, and with no multivariate survival function, no smoothing and no tuning parameter beyond the threshold. The theory of this directional estimator is formulated under an arbitrary marginal standardization and conditions only imposed on the standardized laws, in particular for any max-domain of attraction. We prove uniform consistency and functional weak convergence at the square root of the effective number of joint extremes, allowing the standardization to be estimated. A multiplicative standardization recovers the heavy-tailed theory, whereas a standardization built from the marginal (non-directional) Kaplan--Meier estimators requires no marginal tail model and targets the normalized tail copula itself. The standardization error is negligible for the former, and for the latter under a mild condition on the joint censoring. Simulation studies validate the finite-sample performance of the estimator. An application to the National Flood Insurance Program dataset comprised of claims generated by Hurricane Ian estimates the joint upper tail of building and contents losses from indemnities, which are subject to capping, simultaneously in both coordinates.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.37904 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T17:33:58.780Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).