Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models

Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult. We proposed algorithms that were able to fit Cox models in high dimensi...

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Detalhes bibliográficos

Principais autores:	Frédéric Bertrand , Myriam Maumy-Bertrand
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2021-11-01
coleção:	Frontiers in Big Data
Assuntos:	big data and analytics censored data partial least squares sparse partial least squares regression sparse partial least squares discriminant analysis kernel techniques
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fdata.2021.684794/full
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