Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90600
Title: | Quantile correlation-based variable selection | Authors: | Tang, W Xie, J Lin, Y Tang, N |
Issue Date: | 2022 | Source: | Journal of business and economic statistics, 2022, v. 40, no. 3, p. 1081-1093 | Abstract: | 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. | Keywords: | False discovery rate High dimensionality Quantile correlation Variable selection |
Publisher: | Taylor & Francis Inc. | Journal: | Journal of business and economic statistics | ISSN: | 0735-0015 | EISSN: | 1537-2707 | DOI: | 10.1080/07350015.2021.1899932 | Rights: | © 2021 American Statistical Association This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of business and economic statistics on 21 Apr 2021 (Published online), available online: http://www.tandfonline.com/10.1080/07350015.2021.1899932. |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Tang_Quantile_Correlation-Based_Variable.pdf | Pre-Published version | 506.95 kB | Adobe PDF | View/Open |
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