Gradient-induced Model-free Variable Selection with Composite Quantile Regression

2018 ◽  
Author(s):  
Shaogao Lv ◽  
Xin He ◽  
Junhui Wang
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Free Variable ◽  
Composite Quantile Regression ◽  
Model Free

A model-free variable selection method for reducing the number of redundant variables

Statistics ◽  
2018 ◽  
Vol 52 (6) ◽  
pp. 1212-1248
Author(s):  
Anchao Song ◽  
Tiefeng Ma ◽  
Shaogao Lv ◽  
Changsheng Lin
Keyword(s):  
Variable Selection ◽  
Free Variable ◽  
Selection Method ◽  
Variable Selection Method ◽  
Model Free

scholarly journals On dual model-free variable selection with two groups of variables

2018 ◽  
Vol 167 ◽  
pp. 366-377
Author(s):  
Ahmad Alothman ◽  
Yuexiao Dong ◽  
Andreas Artemiou
Keyword(s):  
Variable Selection ◽  
Free Variable ◽  
Dual Model ◽  
Model Free

scholarly journals Knockoff boosted tree for model-free variable selection

2020 ◽  
Author(s):  
Tao Jiang ◽  
Yuanyuan Li ◽  
Alison A Motsinger-Reif
Keyword(s):  
Variable Selection ◽  
Principal Component ◽  
Free Variable ◽  
Supplementary Information ◽  
Type I ◽  
Test Statistics ◽  
Linear Regression Models ◽  
Model Free ◽  
Tree Models ◽  
Boosted Tree

Abstract Motivation The recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a ‘knockoff’ of each variable tested for exact FDR control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with FDR control. Results We propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. We extend the current knockoff method to model-free variable selection through the use of tree-based models. Additionally, we propose and evaluate two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test and compare these methods with the original knockoff method regarding their ability to control type I errors and power. In simulation tests, we compare the properties and performance of importance test statistics of tree models. The results include different combinations of knockoffs and importance test statistics. We consider scenarios that include main-effect, interaction, exponential and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using Cancer Genome Atlas (TCGA) gene expression data. Our results show improved discrimination between difficult-to-discriminate cancer types. Availability and implementation The proposed algorithm is included in the KOBT package, which is available at https://cran.r-project.org/web/packages/KOBT/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Model-free variable selection

2005 ◽  
Vol 67 (2) ◽  
pp. 285-299 ◽  
Author(s):  
Lexin Li ◽  
R. Dennis Cook ◽  
Christopher J. Nachtsheim
Keyword(s):  
Variable Selection ◽  
Free Variable ◽  
Model Free

scholarly journals Multiple Loci Mapping via Model-free Variable Selection

Biometrics ◽  
2011 ◽  
Vol 68 (1) ◽  
pp. 12-22
Author(s):  
Wei Sun ◽  
Lexin Li
Keyword(s):  
Variable Selection ◽  
Free Variable ◽  
Model Free ◽  
Multiple Loci

Variable selection via composite quantile regression with dependent errors

2014 ◽  
Vol 69 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yanlin Tang ◽  
Xinyuan Song ◽  
Zhongyi Zhu
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Composite Quantile Regression
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