scholarly journals Correlation‐adjusted regression survival scores for high‐dimensional variable selection

2019 ◽  
Vol 38 (13) ◽  
pp. 2413-2427
Author(s):  
Thomas Welchowski ◽  
Verena Zuber ◽  
Matthias Schmid
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Dimensional Variable

scholarly journals False discovery control for penalized variable selections with high-dimensional covariates

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Kevin He ◽  
Xiang Zhou ◽  
Hui Jiang ◽  
Xiaoquan Wen ◽  
Yi Li
Keyword(s):  
Variable Selection ◽  
Linear Models ◽  
Broad Class ◽  
High Dimensional ◽  
High Throughput Data ◽  
False Discovery ◽  
Dimensional Variable ◽  
Selection Algorithms ◽  
False Discoveries ◽  
Linear Regressions

Abstract Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors much exceeding the sample size. Penalized variable selection has emerged as a powerful and efficient dimension reduction tool. However, control of false discoveries (i.e. inclusion of irrelevant variables) for penalized high-dimensional variable selection presents serious challenges. To effectively control the fraction of false discoveries for penalized variable selections, we propose a false discovery controlling procedure. The proposed method is general and flexible, and can work with a broad class of variable selection algorithms, not only for linear regressions, but also for generalized linear models and survival analysis.

scholarly journals High-Dimensional Variable Selection in Meta-Analysis for Censored Data

Biometrics ◽  
2010 ◽  
Vol 67 (2) ◽  
pp. 504-512 ◽  
Author(s):  
Fei Liu ◽  
David Dunson ◽  
Fei Zou
Keyword(s):  
Variable Selection ◽  
Censored Data ◽  
Meta Analysis ◽  
High Dimensional ◽  
Dimensional Variable

Transformed low-rank ANOVA models for high-dimensional variable selection

2018 ◽  
Vol 28 (4) ◽  
pp. 1230-1246 ◽  
Author(s):  
Yoonsuh Jung ◽  
Hong Zhang ◽  
Jianhua Hu
Keyword(s):  
Variable Selection ◽  
Selection Criterion ◽  
Real Data ◽  
Interaction Space ◽  
Low Rank ◽  
High Dimensional ◽  
Detection Accuracy ◽  
Model Framework ◽  
Conventional Solution ◽  
Dimensional Variable

High-dimensional data are often encountered in biomedical, environmental, and other studies. For example, in biomedical studies that involve high-throughput omic data, an important problem is to search for genetic variables that are predictive of a particular phenotype. A conventional solution is to characterize such relationships through regression models in which a phenotype is treated as the response variable and the variables are treated as covariates; this approach becomes particularly challenging when the number of variables exceeds the number of samples. We propose a general framework for expressing the transformed mean of high-dimensional variables in an exponential distribution family via ANOVA models in which a low-rank interaction space captures the association between the phenotype and the variables. This alternative method transforms the variable selection problem into a well-posed problem with the number of observations larger than the number of variables. In addition, we propose a model selection criterion for the new model framework with a diverging number of parameters, and establish the consistency of the selection criterion. We demonstrate the appealing performance of the proposed method in terms of prediction and detection accuracy through simulations and real data analyses.

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