General multiple tests for functional data

doi:10.1007/s10182-025-00549-y

General multiple tests for functional data

2025-12-01

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DOI：10.1007/s10182-025-00549-y

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While there exists several inferential methods for analyzing functional data in factorial designs, there is a lack of statistical tests that are valid (i) in general designs, (ii) under non-restrictive assumptions on the data generating process and (iii) allow for coherent post hoc analyses. In particular, most existing methods assume Gaussianity or equal covariance functions across groups (homoscedasticity) and are only applicable for specific study designs that do not allow for evaluation of interactions. Moreover, almost all strategies published to date are aimed at testing global hypotheses and do not directly allow a more in-depth analysis of multiple local hypotheses. To address the first two problems (i-ii), we propose flexible integral-type test statistics that are applicable in general factorial designs under minimal assumptions on the data generating process. In particular, we neither postulate homoscedasticity nor Gaussianity. To approximate the statistics' null distribution, we adopt a resampling approach and validate it methodologically. Finally, we use our flexible testing framework to (iii) infer several local null hypotheses simultaneously. To allow for powerful data analysis, we thereby take the complex dependencies of the different local test statistics into account. In extensive simulations we confirm that the new methods are flexibly applicable. Two illustrative data analyses complete our study. The new testing procedures are implemented in the R package multiFANOVA, which is available on CRAN.

Keywords:

Analysis of variance (ANOVA)

Functional data

FANOVA

Heteroscedasticity

Multiple testing

Resampling

General multiple tests for functional data

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