A1 Refereed original research article in a scientific journal
Dimension estimation in a spiked covariance model using high-dimensional data augmentation
Authors: Radojičić, U; Virta, J.
Publisher: OXFORD UNIV PRESS
Publication year: 2025
Journal: Biometrika
Article number: asaf052
Volume: 112
Issue: 4
ISSN: 0006-3444
eISSN: 1464-3510
DOI: https://doi.org/10.1093/biomet/asaf052
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1093/biomet/asaf052
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/505616526
We propose a modified, high-dimensional version of a recent dimension estimation procedure that determines the dimension via the introduction of augmented noise variables into the data. Our asymptotic results show that the proposal is consistent in wide, high-dimensional scenarios, and further shed light on why the original method breaks down when the dimension of either the data or the augmentation becomes too large. Simulations and real data are used to demonstrate the superiority of the proposal to competitors both under and outside of the theoretical model.
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Funding information in the publication:
The work of Virta was supported by the Research Council of Finland (335077, 347501, 353769). The work of Radojičić was funded by the Austrian Science Fund (FWF) [10.55776/I5799].
