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...
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: | |
Online Access: | http://link.springer.com/article/10.1186/s12874-017-0291-y |
Similar Items
-
Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer
by: Zhai, Jing, et al.
Published: (2017) -
The Usage of Lasso, Ridge, and Linear Regression to Explore the Most Influential Metabolic Variables that Affect Fasting Blood Sugar in Type 2 Diabetes Patients
by: Farbahari Arash, et al.
Published: (2019-12-01) -
On ridge regression and least absolute shrinkage and selection operator
by: AlNasser, Hassan
Published: (2017) -
Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression
by: Anke Hüls, et al.
Published: (2017-06-01) -
LASSO type penalized spline regression for binary data
by: Muhammad Abu Shadeque Mullah, et al.
Published: (2021-04-01)