Quantile Correlation-based Variable Selection

Bibliographic Data

ID19419294
AuthorsWenlu Tang (0000-0001-9448-578X, Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China), Jinhan Xie (0000-0001-7407-8474, Yunnan University), Yuanyuan Lin (0000-0002-7773-4921, Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China), Niansheng Tang (0000-0001-7033-3845, Yunnan University, corresponding author)
Year2022
Volume40
Issue3
Pages1081-1093
Publication date2022-07-03
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2021.1899932
OpenAlexW3134171954
LanguageEN
References cited34

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

Correlation · Curse of dimensionality · Data mining · Econometrics · Feature selection · Machine learning · Quantile · Statistic · Statistics · Bayesian Methods and Mixture Models · Computer Science · Engineering · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

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