Abstract
We consider two–sample tests for functional data with observations which may be uni– or multi–dimensional. The new methods are formulated as L2–type criteria based on empirical characteristic functions and are convenient from the computational point of view.
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Benko, M., Härdle, W., Kneip, A.: Common functional principal components. Ann. Stat. 37, 1–34 (2009)
Berrendero, J.R., Justel, A., Svarc, M.: Principal components for multivariate functional data. Comput. Stat. Data An. 55, 2619–2634 (2011)
Chiou, J.M.,Müller, H.G.: Linear manifold modeling of multivariate functional data. J. Roy. Stat. Soc. B. 76, 605–626 (2014)
Hall, P., Van Keilegom, I.: Two–sample tests in functional data analysis starting from discrete data. Stat. Sinica. 17, 1511–1531 (2007)
Horváth, L., Kokoszka, P.: Inference for Functional Data with Applications. Springer Series in Statistics, Springer, New York (2012)
Horváth, L., Kokoszka, P., Reeder, R.: Estimation of the mean of functional time series and a two sample problem. J. Roy. Stat. Soc. B. 75, 103–122 (2013)
Jacques, J., Preda, C.: Model–based clustering for multivariate functional data. Comput. Stat. Data An. 71, 92–106 (2014)
Kraus, D., Panaretos, V.M.: Dispersion operators and resistant second–order functional data analysis. Biometrika. 99, 813–832 (2012)
Hušková, M., Meintanis, S.G.: Tests for the multivariate k-sample problem based on the empirical characteristic function. J. NonParametr. Statist. 20, 263–277 (2008)
Meintanis, S.G.: Permutation tests for homogeneity based on the empirical characteristic function. J. Nonparametr. Statist. 17, 583–592 (2005)
Panaretos, V.M., Kraus, D., Maddocks, J.H.: Second–order comparison of Gaussian random functions and the geometry of DNA minicircles. J. Am. Stat. Assoc. 105, 670–682 (2010)
Pomann, G.M., Staicu, A.M., Ghosh, S.: Two–sample hypothesis testing for functional data with application to a diffusion tensor imaging study of multiple sclerosis. J. Roy. Stat. Soc. C-App. 65, 395–414 (2016)
Székely G., Rizzo, M.: Hierarchical clustering via joint between–within distances: Extending Ward’s minimum variance method. J. Classif. 22, 151–183 (2005)
Székely G., Rizzo, M.: Energy statistics: A class of statistics based on distances. J. Stat. Plan. Infer. 143, 249–272 (2013)
Tenreiro, C.: On the choice of the smoothing parameter for the BHEP goodnessof-fit test. Computat. Statist. Dat. Anal. 53, 1038–1053 (2009)
Zhang, C., Peng, H., Zhang, J.T.: Two–sample tests for functional data. Commun. Stat.–Theor. M. 39, 559–578 (2010)
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Jiang, Q., Meintanis, S.G., Zhu, L. (2017). Two-sample tests for multivariate functional data. In: Aneiros, G., G. Bongiorno, E., Cao, R., Vieu, P. (eds) Functional Statistics and Related Fields. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55846-2_19
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DOI: https://doi.org/10.1007/978-3-319-55846-2_19
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