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High-dimensional Four-Level Structured Functional Principal Component Analysis for Unbalanced Level Data with Application to Concussion Data — arXiv:2611.05682

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05682
License (as archived): CC0-1.0
Archived: 2026-11-05T18:27:14.184Z
SHA-256 of the archived text: d658226fe1501c96e4974837d886d7d1a8dc9edc3ecc679648a167c40be62c95
Stable URL of this page: https://metraipolis.com/documento/dc_d3813660ed45

How to cite

High-dimensional Four-Level Structured Functional Principal Component Analysis for Unbalanced Level Data with Application to Concussion Data — arXiv:2611.05682 — arXiv (en), https://arxiv.org/abs/2611.05682 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d3813660ed45 (Metraipolis archive dc_d3813660ed45, sha256 d658226fe1501c96e4974837d886d7d1a8dc9edc3ecc679648a167c40be62c95)

BibTeX

@misc{dcd3813660ed45,
  title = {High-dimensional Four-Level Structured Functional Principal Component Analysis for Unbalanced Level Data with Application to Concussion Data — arXiv:2611.05682},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05682},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d3813660ed45; sha256 d658226fe1501c96e4974837d886d7d1a8dc9edc3ecc679648a167c40be62c95},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05682. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We implement the Structured Functional Principal Component Analysis (SFPCA) framework of Shou et al. (2015) in a four-level nested setting with arbitrarily unbalanced group sizes at every level of the hierarchy. While the SFPCA framework can in principle accommodate deeper hierarchical structures, practical implementations remain challenging when the number of curves is large and the hierarchy is highly unbalanced. Our contributions are twofold. First, we operationalize SFPCA for four hierarchical levels with arbitrary unbalancedness across units. Second, we propose a memory-efficient computational strategy that makes the analysis of large collections of curves feasible on conventional hardware. In particular, we construct the four design-specific matrices required for the Method of Moments (MoM) estimators of the level-specific covariance operators while controlling their memory footprint, which would otherwise grow prohibitively with the number of curves. We illustrate the approach using three subsets of the NCAA-DoD Grand Alliance Concussion Assessment, Research, and Education (CARE) Consortium dataset (2014-2017).

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/2611.05682 — 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 2026-11-05T18:16:49.359Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).