Document Type

Journal Article

Department/Unit

Department of Mathematics

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.

Keywords

Functional hypothesis testing, Functional data, Local power, Nonparametric bootstrap, Smoothing and nonparametric regression

Publication Date

4-2019

Source Publication Title

Computational Statistics and Data Analysis

Volume

132

Start Page

3

End Page

17

Publisher

Elsevier

DOI

10.1016/j.csda.2018.05.004

Link to Publisher's Edition

https://doi.org/10.1016/j.csda.2018.05.004

ISSN (print)

01679473

ISSN (electronic)

18727352

Available for download on Saturday, May 01, 2021

Additional Files

Supplementary data.pdf (279 kB)

Included in

Mathematics Commons

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