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Two-sample tests for multivariate functional data with applications

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  • Qiu, Zhiping
  • Chen, Jianwei
  • Zhang, Jin-Ting

Abstract

Multivariate functional data are frequently obtained in many scientific or industrial areas where several functions for a statistical unit are observed over time. It is often interesting to check if the mean vector functions of two multivariate functional samples are equal. To address this important issue, two global tests for the above two-sample problem for multivariate functional data are proposed and studied. Their asymptotic random expressions under the null and certain local alternative hypotheses are derived and their root-n consistencies are established. Simulation studies show that the proposed two tests generally have higher or not worse powers than some existing competitors. A real data application illustrates the proposed tests.

Suggested Citation

  • Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:csdana:v:157:y:2021:i:c:s0167947320302516
    DOI: 10.1016/j.csda.2020.107160
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    1. Hervé Cardot & Luboš Prchal & Pascal Sarda, 2007. "No effect and lack-of-fit permutation tests for functional regression," Computational Statistics, Springer, vol. 22(3), pages 371-390, September.
    2. Boente, Graciela & Salibián Barrera, Matías & Tyler, David E., 2014. "A characterization of elliptical distributions and some optimality properties of principal components for functional data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 254-264.
    3. Jin-Ting Zhang & Xuehua Liang, 2014. "One-Way anova for Functional Data via Globalizing the Pointwise F-test," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 51-71, March.
    4. Lajos Horváth & Piotr Kokoszka & Ron Reeder, 2013. "Estimation of the mean of functional time series and a two-sample problem," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 103-122, January.
    5. Ana-Maria Staicu & Yingxing Li & Ciprian M. Crainiceanu & David Ruppert, 2014. "Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 932-949, December.
    6. Dennis D. Cox & Jong Soo Lee, 2008. "Pointwise testing with functional data using the Westfall--Young randomization method," Biometrika, Biometrika Trust, vol. 95(3), pages 621-634.
    7. Sara López-Pintado & Ying Sun & Juan Lin & Marc Genton, 2014. "Simplicial band depth for multivariate functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 321-338, September.
    8. Livio Corain & Viatcheslav Melas & Andrey Pepelyshev & Luigi Salmaso, 2014. "New insights on permutation approach for hypothesis testing on functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 339-356, September.
    9. Istem Koymen Keser & Ipek Deveci Kocako�, 2015. "Smoothed functional canonical correlation analysis of humidity and temperature data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(10), pages 2126-2140, October.
    10. Antoniadis, Anestis & Sapatinas, Theofanis, 2007. "Estimation and inference in functional mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4793-4813, June.
    11. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
    12. Gerda Claeskens & Mia Hubert & Leen Slaets & Kaveh Vakili, 2014. "Multivariate Functional Halfspace Depth," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 411-423, March.
    13. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    14. Spitzner D.J. & Marron J.S. & Essick G.K., 2003. "Mixed-Model Functional ANOVA for Studying Human Tactile Perception," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 263-272, January.
    15. Zhang, Jin-Ting & Cheng, Ming-Yen & Wu, Hau-Tieng & Zhou, Bu, 2019. "A new test for functional one-way ANOVA with applications to ischemic heart screening," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 3-17.
    16. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Rejoinder to ‘multivariate functional outlier detection’," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 269-277, July.
    17. Tomasz Górecki & Mirosław Krzyśko & Łukasz Waszak & Waldemar Wołyński, 2018. "Selected statistical methods of data analysis for multivariate functional data," Statistical Papers, Springer, vol. 59(1), pages 153-182, March.
    18. Dai, Wenlin & Genton, Marc G., 2019. "Directional outlyingness for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 50-65.
    19. Tomasz Górecki & Łukasz Smaga, 2015. "A comparison of tests for the one-way ANOVA problem for functional data," Computational Statistics, Springer, vol. 30(4), pages 987-1010, December.
    20. Manuel Febrero-Bande & Wenceslao González-Manteiga, 2013. "Generalized additive models for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 278-292, June.
    21. Fremdt, Stefan & Horváth, Lajos & Kokoszka, Piotr & Steinebach, Josef G., 2014. "Functional data analysis with increasing number of projections," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 313-332.
    22. Shuichi Tokushige & Hiroshi Yadohisa & Koichi Inada, 2007. "Crisp and fuzzy k-means clustering algorithms for multivariate functional data," Computational Statistics, Springer, vol. 22(1), pages 1-16, April.
    23. Jaromir Antoch & Lubos Prchal & Maria Rosaria De Rosa & Pascal Sarda, 2010. "Electricity consumption prediction with functional linear regression using spline estimators," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2027-2041.
    24. Alessia Pini & Simone Vantini, 2016. "The interval testing procedure: A general framework for inference in functional data analysis," Biometrics, The International Biometric Society, vol. 72(3), pages 835-845, September.
    25. Raymond K. W. Wong & Yehua Li & Zhengyuan Zhu, 2019. "Partially Linear Functional Additive Models for Multivariate Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 406-418, January.
    26. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    27. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
    28. J. Cuesta-Albertos & M. Febrero-Bande, 2010. "A simple multiway ANOVA for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(3), pages 537-557, November.
    29. Jia Guo & Bu Zhou & Jin-Ting Zhang, 2019. "New Tests for Equality of Several Covariance Functions for Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1251-1263, July.
    30. Peter Hall, 2002. "Permutation tests for equality of distributions in high-dimensional settings," Biometrika, Biometrika Trust, vol. 89(2), pages 359-374, June.
    31. Chiou, Jeng-Min & Yang, Ya-Fang & Chen, Yu-Ting, 2016. "Multivariate functional linear regression and prediction," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 301-312.
    32. Clara Happ & Sonja Greven, 2018. "Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 649-659, April.
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