High-dimensional local polynomial regression with variable selection and dimension reduction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s11222-023-10308-1.pdf
Reference27 articles.
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2. Chen, X., Zou, C., Cook, R.D.: Coordinate-independent sparse sufficient dimension reduction and variable selection. Ann. Stat. 38, 3696–3723 (2010)
3. Chen, J., Zhang, C., Kosorok, M.R., Liu, Y.: Double sparsity kernel learning with automatic variable selection and data extraction. Stat. Interface 11, 401–420 (2018)
4. Conn, D., Li, G.: An oracle property of the Nadaraya–Watson kernel estimator for high dimensional nonparametric regression. Scand. J. Stat. 46, 735–764 (2019)
5. Cook, R.D., Li, B.: Dimension reduction for conditional mean in regression. Ann. Stat. 30, 455–474 (2002)
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