High‐dimensional principal component analysis with heterogeneous missingness

We study the problem of high‐dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed‐proportion weighted (OPW) estimator of the leading principal components can (nearly) attain the minimax optimal rate of...

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Detalles Bibliográficos
Autores principales: Zhu, Ziwei, Wang, Tengyao, Samworth, Richard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098677/
https://www.ncbi.nlm.nih.gov/pubmed/37065873
http://dx.doi.org/10.1111/rssb.12550

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098677/
https://www.ncbi.nlm.nih.gov/pubmed/37065873
http://dx.doi.org/10.1111/rssb.12550

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