Quantile Correlation-based Variable Selection

This article is concerned with identifying important features in high-dimensional data analysis, especially when there are complex relationships among predictors. Without any specification of an actual model, we first introduce a multiple testing procedure based on the quantile correlation to select important predictors in high dimensionality. The quantile-correlation statistic is able to capture a wide range of dependence. A stepwise procedure is studied for further identifying important variables. Moreover, a sure independent screening based on the quantile correlation is developed in handling ultrahigh dimensional data. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.

Automated summary

This article focuses on identifying important features in high-dimensional data analysis and introduces a multiple testing procedure based on quantile correlation. A stepwise procedure and sure independent screening method using quantile correlation are also developed. Numerical studies show that these methods perform well in practical settings.