Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer

Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer

Abstract Background Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects mus...

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Bibliographic Details
Main Authors: Jing Zhai, Chiu-Hsieh Hsu, Z. John Daye
Format: Article
Language:English
Published: BMC 2017-01-01
Series:BMC Medical Research Methodology
Subjects:
Gene expression analysis
Lasso
Linear models
Penalized regression
Ridge
Variable selection
Online Access:http://link.springer.com/article/10.1186/s12874-017-0291-y

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http://link.springer.com/article/10.1186/s12874-017-0291-y

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