TY - JOUR
T1 - A new test for functional one-way ANOVA with applications to ischemic heart screening
AU - Zhang, Jin Ting
AU - CHENG, Ming-Yen
AU - Wu, Hau Tieng
AU - Zhou, Bu
N1 - Funding Information:
Zhang and Zhou's research was supported by the National University of Singapore research grant R-155-000-164-112. Cheng's research was supported by the Hong Kong Baptist University grant RG(R)/17-18/01-MATH and the Ministry of Science and Technology grant 104-2118-M-002-005-MY3. Wu's research was supported by AFOSR grant FA9550-09-1-0551, NSF grant CCF-0939370, and FRG grant DMS-1160319. We thank Dr Chi-Jen Tseng (Fooyin University Hospital) for providing us with the ischemic heart dataset. The authors thank two reviewers and the AE for their invaluable comments and suggestions.
Funding Information:
Zhang and Zhou’s research was supported by the National University of Singapore research grant R-155-000-164-112 . Cheng’s research was supported by the Hong Kong Baptist University grant RG(R)/17-18/01-MATH and the Ministry of Science and Technology grant 104-2118-M-002-005-MY3 . Wu’s research was supported by AFOSR grant FA9550-09-1-0551 , NSF grant CCF-0939370 , and FRG grant DMS-1160319 . We thank Dr Chi-Jen Tseng (Fooyin University Hospital) for providing us with the ischemic heart dataset. The authors thank two reviewers and the AE for their invaluable comments and suggestions.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - Functional data
KW - Functional hypothesis testing
KW - Local power
KW - Nonparametric bootstrap
KW - Smoothing and nonparametric regression
UR - http://www.scopus.com/inward/record.url?scp=85048165498&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2018.05.004
DO - 10.1016/j.csda.2018.05.004
M3 - Journal article
AN - SCOPUS:85048165498
SN - 0167-9473
VL - 132
SP - 3
EP - 17
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -