GRID: A variable selection and structure discovery method for high dimensional nonparametric regression

2020 ◽  
Vol 48 (3) ◽  
pp. 1848-1874
Author(s):  
Francesco Giordano ◽  
Soumendra Nath Lahiri ◽  
Maria Lucia Parrella
Keyword(s):  
Variable Selection ◽  
Nonparametric Regression ◽  
High Dimensional ◽  
Discovery Method

scholarly journals Iterative Variable Selection for High-Dimensional Data: Prediction of Pathological Response in Triple-Negative Breast Cancer

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 222
Author(s):  
Juan C. Laria ◽  
M. Carmen Aguilera-Morillo ◽  
Enrique Álvarez ◽  
Rosa E. Lillo ◽  
Sara López-Taruella ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Variable Selection ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
A Priori ◽  
Simulated Data ◽  
Point Of View ◽  
High Dimensional ◽  
Whole Genome ◽  
Genome Context

Over the last decade, regularized regression methods have offered alternatives for performing multi-marker analysis and feature selection in a whole genome context. The process of defining a list of genes that will characterize an expression profile remains unclear. It currently relies upon advanced statistics and can use an agnostic point of view or include some a priori knowledge, but overfitting remains a problem. This paper introduces a methodology to deal with the variable selection and model estimation problems in the high-dimensional set-up, which can be particularly useful in the whole genome context. Results are validated using simulated data and a real dataset from a triple-negative breast cancer study.

Variable selection in high-dimensional double generalized linear models

2012 ◽  
Vol 55 (2) ◽  
pp. 327-347 ◽  
Author(s):  
Dengke Xu ◽  
Zhongzhan Zhang ◽  
Liucang Wu
Keyword(s):  
Variable Selection ◽  
Generalized Linear Models ◽  
Linear Models ◽  
High Dimensional
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