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参考文献：論文（1/2） 41 • Cardot, H. and Sarda, P. (2008). Varying-coefficient functional linear regression models. Communications in Statistics. Theory and Methods, 37(20), 3186–3203. • Chen, Y., Lin, Z., and Müller, H.-G. (2021). Wasserstein Regression, Journal of the American Statistical Association, In press. • Guan, T., Lin, Z., Cao, J., Guan, T., Lin, Z., and Cao, J. (2020). Estimating Truncated Functional Linear Models With a Nested Group Bridge Approach. Journal of Computational and Graphical Statistics, 29(3), 620–628. • Hall, P. and Hooker, G. (2016). Truncated linear models for functional data. Journal of the Royal Statistical Society Series B 78(3), 637–653. • Hyndman, R. J. and Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics and Data Analysis, 51(10), 4942–4956. • Jacques, J. and Preda, C. (2014). Functional data clustering: a survey. Advances in Data Analysis and Classification, 8, 231–255. • James, G., Hastie, T., and Sugar, C. (2000). Principal component models for sparse functional data. Biometrika, 87(3), 587–602. • James, G., Wang, J., and Zhu, J. (2009). Functional linear regression that’s interpretable. The Annals of Statistics, 37(5), 2083–2108.
参考文献：論文（2/2） • Kayano, M., Matsui, H., Yamaguchi, R., Imoto, S., and Miyano, S. (2016). Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time dependent biomarker detection. Biostatistics, 17, 235–248. • Lin, Z., Cao, J., Wang, L., and Wang, H. (2017). Locally Sparse Estimator for Functional Linear Regression Models. Journal of Computational and Graphical Statistics, 26(2):306–318. • Matsui, H., Araki, Y., and Konishi, S. (2008). Multivariate regression modeling for functional data. Journal of Data Science, 6(3), 313–331. • Petersen, A., Müller, H.-G. (2019). Wasserstein covariance for multiple random densities, Biometrika, 106, 339–351. • Ramsay, J. (1996). Principal differential analysis. Journal of the Royal Statistical Society Series B, 58, 495–508. • Ramsay, J. and Dalzell, C. (1991). Some tools for functional data analysis. Journal of the Royal Statistical Society Series B, 53, 539–572. • Sangalli, L.M., Secchi, P., Vantini, S. and Vitelli, V. (2010). K-mean alignment for curve clustering, Computational Statistics and Data Analysis, 54, 1219-1233 • Wu, Y., Fan, J., and Müller, H. (2010b). Varying-coefficient functional linear regression. Bernoulli, 16(3):730–758. • Zhou, J., Wang, N.-y., and Wang, N. (2013). Functional linear model with zero-value coefficient function at sub-regions. Statistica Sinica, 23, 25–50. 42
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