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Article summary:

1. Ultrahigh-dimensional data analysis is becoming increasingly common in scientific research.

2. Sure independence screening (SIS) is a procedure used to screen out uninfluential predictors while retaining all influential variables.

3. This article proposes a selection procedure based on sample-splitting to determine the threshold for error rate control.

Article analysis:

The article provides an overview of the challenges posed by ultrahigh-dimensional data analysis and introduces the sure independence screening (SIS) procedure as a way to efficiently explore relationships between response variables and predictor vectors. The article then proposes a selection procedure based on sample-splitting to determine the threshold for error rate control, which is presented as an effective solution to this problem.

The article appears to be well researched and provides a comprehensive overview of the topic, with references to relevant literature and studies. However, there are some potential biases that should be noted. For example, the article does not discuss any potential risks associated with using SIS or other feature screening procedures, nor does it present any counterarguments or alternative solutions that could be explored. Additionally, there is no discussion of how this proposed selection procedure might affect accuracy or performance in different contexts or scenarios, so it is difficult to assess its reliability in practice. Furthermore, the article does not provide any evidence for its claims about the effectiveness of this selection procedure, so it is unclear whether these claims are supported by empirical data or simply theoretical assumptions.

In conclusion, while this article provides an informative overview of feature screening procedures and presents an interesting proposal for selecting thresholds for error rate control, there are some potential biases that should be taken into consideration when assessing its trustworthiness and reliability.