论文信息 - One‐Way anova for Functional Data via Globalizing the Pointwise F‐test

One‐Way anova for Functional Data via Globalizing the Pointwise F‐test

type="main" xml:id="sjos12025-abs-0001"> In this paper, we propose and study a new global test, namely, GPF test, for the one-way anova problem for functional data, obtained via globalizing the usual pointwise F-test. The asymptotic random expressions of the test statistic are derived, and its asymptotic power is investigated. The GPF test is shown to be root-n consistent. It is much less computationally intensive than a parametric bootstrap test proposed in the literature for the one-way anova for functional data. Via some simulation studies, it is found that in terms of size-controlling and power, the GPF test is comparable with two existing tests adopted for the one-way anova problem for functional data. A real data example illustrates the GPF test.

Jin-Ting Zhang | Jin-Ting Zhang | X. Liang | Xuehua Liang | Xuehua Liang

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