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VARIABLE SELECTION FOR HIGH DIMENSIONAL MULTIVARIATE OUTCOMES

We consider variable selection for high-dimensional multivariate regression using penalized likelihoods when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, we consider variable selection when a working precision matrix is used and when...

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Publicat a:Stat Sin
Autors principals: Sofer, Tamar, Dicker, Lee, Lin, Xihong
Format: Artigo
Idioma:Inglês
Publicat: 2014
Matèries:
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC5478010/
https://ncbi.nlm.nih.gov/pubmed/28642637
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.5705/ss.2013.019
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