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A new test for functional one-way ANOVA with applications to ischemic heart screening

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  • Zhang, Jin-Ting
  • Cheng, Ming-Yen
  • Wu, Hau-Tieng
  • Zhou, Bu

Abstract

Motivated by an ischemic heart screening problem, a new global test for one-way ANOVA in functional data analysis is studied. The test statistic is taken as the maximum of the pointwise F-test statistic over the interval the functional responses are observed. Nonparametric bootstrap, which is applicable in more general situations and easier to implement than parametric bootstrap, is employed to approximate the null distribution and to obtain an approximate critical value. Under mild conditions, asymptotically our test has the correct level and is root-n consistent in detecting local alternatives. Simulation studies show that the proposed test outperforms several existing tests in terms of both size control and power when the correlation between observations at any two different points is high or moderate, and it is comparable with the competitors otherwise. Application to an ischemic heart dataset suggests that resting electrocardiogram signals may contain enough information for ischemic heart screening at outpatient clinics, without the help of stress tests required by the current standard procedure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:132:y:2019:i:c:p:3-17
    DOI: 10.1016/j.csda.2018.05.004
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    References listed on IDEAS

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    Cited by:

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    3. Jiménez-Gamero, M. Dolores & Franco-Pereira, Alba M., 2021. "Testing the equality of a large number of means of functional data," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    4. Balogoun, Armando Sosthène Kali & Nkiet, Guy Martial & Ogouyandjou, Carlos, 2021. "Asymptotic normality of a generalized maximum mean discrepancy estimator," Statistics & Probability Letters, Elsevier, vol. 169(C).
    5. Łukasz Smaga & Jin‐Ting Zhang, 2020. "Linear hypothesis testing for weighted functional data with applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 493-515, June.
    6. Jing Zhao & Sanying Feng & Yuping Hu, 2022. "Two-Sample Hypothesis Test for Functional Data," Mathematics, MDPI, vol. 10(21), pages 1-16, November.
    7. Qiu, Zhiping & Fan, Jiangyuan & Zhang, Jin-Ting & Chen, Jianwei, 2024. "Tests for equality of several covariance matrix functions for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    8. Hlávka, Zdeněk & Hlubinka, Daniel & Koňasová, Kateřina, 2022. "Functional ANOVA based on empirical characteristic functionals," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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