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Analytically Calibrated Upper L-Statistics for High-Dimensional Mean Change-Point Testing — arXiv:2611.04364

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04364
License (as archived): CC0-1.0
Archived: 2026-11-04T21:54:46.072Z
SHA-256 of the archived text: d255be9269f0149eb3e3ce15640cda641ed6bfd500379fc422e954b6385458a2
Stable URL of this page: https://metraipolis.com/documento/dc_2efe9cd2f401

How to cite

Analytically Calibrated Upper L-Statistics for High-Dimensional Mean Change-Point Testing — arXiv:2611.04364 — arXiv (en), https://arxiv.org/abs/2611.04364 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2efe9cd2f401 (Metraipolis archive dc_2efe9cd2f401, sha256 d255be9269f0149eb3e3ce15640cda641ed6bfd500379fc422e954b6385458a2)

BibTeX

@misc{dc2efe9cd2f401,
  title = {Analytically Calibrated Upper L-Statistics for High-Dimensional Mean Change-Point Testing — arXiv:2611.04364},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04364},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2efe9cd2f401; sha256 d255be9269f0149eb3e3ce15640cda641ed6bfd500379fc422e954b6385458a2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04364. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper develops upper L-statistic methods for testing and localizing a high-dimensional mean change. At each candidate split, the proposed statistics rank the squared studentized CUSUM values across coordinates and aggregate either a fixed number or a fixed proportion of the largest entries. This construction connects max-type and sum-type procedures and provides sensitivity to a wide range of sparsity regimes. We establish asymptotic null distributions under an independent-component representation allowing weak cross-sectional dependence. The limiting laws are Poisson tangent processes for fixed ranks and Gaussian-process or Gumbel laws for proportional ranks. To improve finite-sample performance, we develop analytic calibrations for marginal variance estimation, cross-sectional dependence, extreme clustering, and skewness, and establish their first-order validity. We combine calibrated p-values from a matched pair of fixed- and proportional-order statistics using the Cauchy combination rule to adapt to unknown sparsity. For localization, the estimated change point is selected from the component scan with the smallest p-value. Simulation studies demonstrate generally satisfactory size calibration, competitive detection power, and effective localization across different sparsity levels. An application to semiconductor manufacturing data further illustrates the practical utility of the proposed methods.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2611.04364 — 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 2026-11-04T21:39:49.510Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).