An RKHS model for variable selection in functional linear regression
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DOI: 10.1016/j.jmva.2018.04.008
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References listed on IDEAS
- Hao Ji & Hans-Georg Müller, 2017. "Optimal designs for longitudinal and functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 859-876, June.
- Fraiman, Ricardo & Gimenez, Yanina & Svarc, Marcela, 2016. "Feature selection for functional data," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 191-208.
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Cited by:
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- Chen, Di-Rong & Cheng, Kun & Liu, Chao, 2022. "Framelet block thresholding estimator for sparse functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
- Yang, Yang & Yang, Yanrong & Shang, Han Lin, 2022. "Feature extraction for functional time series: Theory and application to NIR spectroscopy data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
- Liang, Weijuan & Zhang, Qingzhao & Ma, Shuangge, 2023. "Locally sparse quantile estimation for a partially functional interaction model," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
- Dominik Poß & Dominik Liebl & Alois Kneip & Hedwig Eisenbarth & Tor D. Wager & Lisa Feldman Barrett, 2020. "Superconsistent estimation of points of impact in non‐parametric regression with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1115-1140, September.
- Aneiros, Germán & Novo, Silvia & Vieu, Philippe, 2022. "Variable selection in functional regression models: A review," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
- Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
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Keywords
Feature selection; Functional linear regression; Impact points; Variable selection;All these keywords.
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