Comments on: shape-based functional data analysis
Author:
Funder
Deutsche Forschungsgemeinschaft
Humboldt-Universität zu Berlin
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s11749-023-00901-x.pdf
Reference15 articles.
1. Ahn K, Tucker JD, Wu W, Srivastava A (2020) Regression models using shapes of functions as predictors. Comput Stat Data Anal 151:107017
2. Cornea E, Zhu H, Kim P, Ibrahim JG (2017) Regression models on Riemannian symmetric spaces. J R Stat Soc Ser B Stat Methodol 79(2):463–482
3. Dryden IL, Mardia KV (2016) Statistical shape analysis: with applications in R, 2nd edn. Wiley, Chichester
4. Fletcher PT (2013) Geodesic regression and the theory of least squares on Riemannian manifolds. Int J Comput Vision 105(2):171–185
5. Hadjipantelis PZ, Aston JA, Müller H-G, Evans JP (2015) Unifying amplitude and phase analysis: a compositional data approach to functional multivariate mixed-effects modeling of Mandarin Chinese. J Am Stat Assoc 110(510):545–559
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