References
- Ait-Saïdi, A., F. Ferraty, R. Kassa, and P. Vieu. 2008. Cross-validated estimations in the single-functional index model. Statistics 42 (6):475–94. doi:https://doi.org/10.1080/02331880801980377.
- Cai, T. T., and P. Hall. 2006. Prediction in functional linear regression. The Annals of Statistics 34 (5):2159–79. doi:https://doi.org/10.1214/009053606000000830.
- Cardot, H., F. Ferraty, A. Mas, and P. Sarda. 2003. Testing hypothesis in the functional linear model. Scandinavian Journal of Statistics 30 (1):241–55. doi:https://doi.org/10.1111/1467-9469.00329.
- Chen, D., P. Hall, and H.-G. Müller. 2011. Single and multiple index functional regression models with nonparametric link. The Annals of Statistics 39 (3):1720–47. doi:https://doi.org/10.1214/11-AOS882.
- Dou, W. W., D. Pollard, and H. H. Zhou. 2012. Estimation in functional regression for general exponential families. The Annals of Statistics 40 (5):2421–51. doi:https://doi.org/10.1214/12-AOS1027.
- Ferraty, F., and P. Vieu. 2006. Nonparametric functional data analysis. New York: Springer.
- Ferraty, F., and Y. Romain. 2011. The oxford handbook of functional data analysis. Oxford: Oxford University Press.
- Hall, P., and M. Hosseini-Nasab. 2006. On properties of functional principal components analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68 (1):109–26. doi:https://doi.org/10.1111/j.1467-9868.2005.00535.x.
- Hall, P., H. G., Müller, and J.-L. Wang. 2006. Properties of principal compo-nent methods for functional and longitudinal data analysis. The Annals of Statistics 34 (3):1493–517. doi:https://doi.org/10.1214/009053606000000272.
- Hall, P., and J. L. Horowitz. 2007. Methodology and convergence rates for functional linear regression. The Annals of Statistics 35 (1):70–91. doi:https://doi.org/10.1214/009053606000000957.
- Li, Y. H., N. Wang, and R. J. Carroll. 2010. Generalized functional linear models with semiparametric single-index interactions. Journal of the American Statistical Association 105 (490):621–33. doi:https://doi.org/10.1198/jasa.2010.tm09313.
- Li, Y., and T. Hsing. 2010. Deciding the dimension of effective dimension reduction space for functional and high-dimensional data. The Annals of Statistics 38 (5):3028–62. doi:https://doi.org/10.1214/10-AOS816.
- James, G. M. 2002. Generalized linear models with functional predictors. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64 (3):411–32. doi:https://doi.org/10.1111/1467-9868.00342.
- Li, Y., and T. Hsing. 2010. Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data. The Annals of Statistics 38 (6):3321–51. doi:https://doi.org/10.1214/10-AOS813.
- Müller, H.-G., and U. Stadtmüller. 2005. Generalized functional linear models. The Annals of Statistics 33 (2):774–805. doi:https://doi.org/10.1214/009053604000001156.
- Müller, H.-G., and F. Yao. 2008. Functional additive models. Journal of the American Statistical Association 103 (484):1534–44. doi:https://doi.org/10.1198/016214508000000751.
- Müller, H.-G., Y. Wu, and F. Yao. 2013. Continuously additive models for nonlinear functional regression. Biometrika 100:607–22. doi:https://doi.org/10.1093/biomet/ast004.
- Ramsay, J., and B. Silverman. 2005. Functional data analysis. 2nd ed. Berlin: Springer.
- Yao, F., H.-G. Müller, and J. L. Wang. 2005. Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association 100 (470):577–90. doi:https://doi.org/10.1198/016214504000001745.
- Yao, F., H.-G. Müller, and J. L. Wang. 2005. Functional linear regression analysis for longitudinal data. The Annals of Statistics 33 (6):2873–903. doi:https://doi.org/10.1214/009053605000000660.
- Yao, F., and H.-G. Müller. 2010. Functional quadratic regression. Biometrika 97 (1):49–64. doi:https://doi.org/10.1093/biomet/asp069.
- Zhang, X., and J. L. Wang. 2015. Varying-coefficient additive models for functional data. Biometrika 102 (1):15–32. doi:https://doi.org/10.1093/biomet/asu053.