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Full article: Model-Free Conditional Feature Screening with FDR Control

Source: tandfonline.com

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Summary Analysis Research

Article summary:

1. This article proposes a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data.

2. The proposed method is robust to heavy-tailed responses and predictors, and enjoys sure screening and ranking consistency properties under mild regularity conditions.

3. Simulated examples and real data analysis show that the proposed method performs very well compared with existing works.

Article analysis:

The article provides a comprehensive overview of the proposed model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The authors provide theoretical guarantees for the proposed method using martingale theory and empirical process techniques, as well as simulated examples and real data analysis to demonstrate its effectiveness compared to existing works.

The article appears to be reliable in terms of its content, however there are some potential biases that should be noted. For example, the authors do not explore any counterarguments or alternative methods that could be used in place of their proposed approach. Additionally, they do not discuss any possible risks associated with their approach or note any limitations of their work. Furthermore, the authors do not present both sides of the argument equally; instead they focus solely on promoting their own approach without considering other perspectives or approaches that could be taken.

In conclusion, while this article appears to be reliable in terms of its content, it does have some potential biases that should be noted when evaluating its trustworthiness and reliability.

Topics for further research:

Alternative methods for ultra-high dimensional data Risks associated with model-free conditional feature screening Limitations of false discovery rate control Counterarguments to proposed model-free conditional feature screening Martingale theory and empirical process techniques Comparison of existing works to proposed model-free conditional feature screening