Local inference for functional linear mixed models
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Local inference for functional linear mixed models. / Pini, Alessia; Sørensen, Helle; Tolver, Anders; Vantini, Simone.
I: Computational Statistics and Data Analysis, Bind 181, 107688, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Local inference for functional linear mixed models
AU - Pini, Alessia
AU - Sørensen, Helle
AU - Tolver, Anders
AU - Vantini, Simone
N1 - Publisher Copyright: © 2023 Elsevier B.V.
PY - 2023
Y1 - 2023
N2 - The problem of performing inference on the parameters of a functional mixed effect model for multivariate functional data is addressed, motivated by the analysis of 3D acceleration curves of trotting horses. Inference is performed in a local perspective, i.e., defining an adjusted p-value function on the same domain as the data. Such adjusted p-value functions can be thresholded at level α to select the regions of the domain and the coordinates of functional data presenting statistically significant effects. The probability of wrongly selecting as significant a region of the domain, and/or a coordinate of functional data where the null hypothesis is true, is always lower than the pre-specified level α due to the interval-wise control of the family-wise error rate. The procedure is based on nonparametric permutation tests, based on different permutation strategies. It is shown by simulations that all strategies proposed gain in power by taking random effects into account in permutations. Finally, the procedure is applied to the acceleration curves of trotting horses for testing differences between different levels of induced lameness. The method can clearly identify group differences.
AB - The problem of performing inference on the parameters of a functional mixed effect model for multivariate functional data is addressed, motivated by the analysis of 3D acceleration curves of trotting horses. Inference is performed in a local perspective, i.e., defining an adjusted p-value function on the same domain as the data. Such adjusted p-value functions can be thresholded at level α to select the regions of the domain and the coordinates of functional data presenting statistically significant effects. The probability of wrongly selecting as significant a region of the domain, and/or a coordinate of functional data where the null hypothesis is true, is always lower than the pre-specified level α due to the interval-wise control of the family-wise error rate. The procedure is based on nonparametric permutation tests, based on different permutation strategies. It is shown by simulations that all strategies proposed gain in power by taking random effects into account in permutations. Finally, the procedure is applied to the acceleration curves of trotting horses for testing differences between different levels of induced lameness. The method can clearly identify group differences.
KW - Domain selection
KW - Horse gait pattern
KW - Interval-wise error rate
KW - Multiple testing
KW - Permutation tests
KW - Random effects
UR - http://www.scopus.com/inward/record.url?scp=85147544135&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2022.107688
DO - 10.1016/j.csda.2022.107688
M3 - Journal article
AN - SCOPUS:85147544135
VL - 181
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
M1 - 107688
ER -
ID: 336075270