The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Feature Screening via Distance Correlation Learning
Open accessAbstract
This paper is concerned with screening features in ultrahigh dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the sure independence screening procedure based on the Pearson correlation (SIS, for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008) established the sure screening property for the SIS based on linear models, but the sure screening property is valid for the DC-SIS under more general settings including linear models. Furthermore, the implementation of the DC-SIS does not require model specification (e.g., linear model or generalized linear model) for responses or predictors. This is a very appealing property in ultrahigh dimensional data analysis. Moreover, the DC-SIS can be used directly to screen grouped predictor variables and for multivariate response variables. We establish the sure screening property for the DC-SIS, and conduct simulations to examine its finite sample performance. Numerical comparison indicates that the DC-SIS performs much better than the SIS in various models. We also illustrate the DC-SIS through a real data example.
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Self-citationThe adaptive lasso and its oracle properties
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Székely, G. J., & Rizzo, M. L. (2013) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2013.03.018 · Cited by 695
Kong, J., Klein, B. E. K., Klein, R., Lee, K. E., & Wahba, G. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1217269109 · Cited by 34
Partial distance correlation with methods for dissimilarities
Székely, G. J., & Rizzo, M. L. (2014) · The Annals of Statistics · doi:10.1214/14-aos1255 · Cited by 34
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Metadata
- Authors
- Li, R., Zhong, W., & Zhu, L.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2012-06
- Volume
- 107
- Issue
- 499
- Pages
- 1129-1139
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2164092415
- Cited by
- 747
- References
- 36
- FWCI
- 20.08
- Type
- journal article
- Language
- en
- Open access
- green
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