References

1. Cook, R. D. (1998). Regression graphics: Ideas for studying regressions through graphics (Vol. 318). John Wiley & Sons. [Crossref], [Google Scholar]
2. Li, K.-C., & Duan, N. (1989). Regression analysis under link violation. The Annals of Statistics, 17(3), 1009–1052. https://doi.org/10.1214/aos/1176347254 [Crossref], [Web of Science ®], [Google Scholar]
3. Mai, Q., & Zhang, X. (2019). An iterative penalized least squares approach to sparse canonical correlation analysis. Biometrics, 75(3), 734–744. https://doi.org/10.1111/biom.v75.3 doi: 10.1111/biom.13043 [Crossref], [Web of Science ®], [Google Scholar]
4. Neykov, M., Lin, Q., & Liu, J. S. (2016). Signed support recovery for single index models in high-dimensions. Annals of Mathematical Sciences and Applications, 1(2), 379–426. https://doi.org/10.4310/AMSA.2016.v1.n2.a5 [Crossref], [Web of Science ®], [Google Scholar]
5. Neykov, M., Liu, J. S., & Cai, T. (2016). L1-regularized least squares for support recovery of high dimensional single index models with gaussian designs. The Journal of Machine Learning Research, 17(1), 2976–3012. [Google Scholar]
6. Ni, L., Cook, R. D., & Tsai, C.-L. (2005). A note on shrinkage sliced inverse regression. Biometrika, 92(1), 242–247. https://doi.org/10.1093/biomet/92.1.242 [Crossref], [Web of Science ®], [Google Scholar]
7. Tan, K., Shi, L., & Yu, Z. (2020). Sparse sir: Optimal rates and adaptive estimation. The Annals of Statistics, 48(1), 64–85. https://doi.org/10.1214/18-AOS1791 [Crossref], [Web of Science ®], [Google Scholar]
8. Zhou, J., & He, X. (2008). Dimension reduction based on constrained canonical correlation and variable filtering. The Annals of Statistics, 36(4), 1649–1668. https://doi.org/10.1214/07-AOS529 [Crossref], [Web of Science ®], [Google Scholar]