A new robust model-free feature screening method for ultra-high dimensional right censored data

2020 ◽  
pp. 1-19
Author(s):  
Yi Liu ◽  
Xiaolin Chen
Keyword(s):  
Censored Data ◽  
Screening Method ◽  
High Dimensional ◽  
Right Censored Data ◽  
Feature Screening ◽  
Model Free ◽  
Robust Model

scholarly journals Analysis of microarray right-censored data through fused sliced inverse regression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jae Keun Yoo
Keyword(s):  
Dimension Reduction ◽  
Censored Data ◽  
Sliced Inverse Regression ◽  
High Dimensional ◽  
Sufficient Dimension Reduction ◽  
Inverse Regression ◽  
Right Censored Data ◽  
Practical Advantage ◽  
Linear Projection ◽  
Lower Dimensional

Abstract Sufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits. Recently, a fused approach for SIR is proposed to relieve this weakness, which fuses the kernel matrices computed by the SIR application from various numbers of slices. In the paper, the fused SIR is applied to a large-p-small n regression of a high-dimensional microarray right-censored data to show its practical advantage over usual SIR application. Through model validation, it is confirmed that the fused SIR outperforms the SIR with any number of slices under consideration.

Robust model-free feature screening for ultrahigh dimensional surrogate data

2019 ◽  
Vol 90 (3) ◽  
pp. 550-569 ◽  
Author(s):  
Peng Lai ◽  
Yuanxing Chen ◽  
Jie Zhang ◽  
Bingying Dai ◽  
Qingzhao Zhang
Keyword(s):  
Surrogate Data ◽  
Feature Screening ◽  
Model Free ◽  
Robust Model

scholarly journals Weighted Mean Squared Deviation Feature Screening for Binary Features

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 335 ◽  
Author(s):  
Gaizhen Wang ◽  
Guoyu Guan
Keyword(s):  
Binary Classification ◽  
Pearson Correlation ◽  
Asymptotic Properties ◽  
Screening Method ◽  
Log P ◽  
Power Law Distribution ◽  
Weighted Mean ◽  
Feature Screening ◽  
Model Free ◽  
Binary Features

In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption log p = o ( n ) . The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.

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