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Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with interval-wise testing
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
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2023 (Engelska)Ingår i: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, Vol. 107, s. 369-392Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The recent sports science literature conveys a growing interest in robust statistical methods to analyze smooth, regularly-sampled functional data. This paper focuses on the inferential problem of identifying the parts of a functional domain where two population means differ. We considered four approaches recently used in sports science: interval-wise testing (IWT), statistical parametric mapping (SPM), statistical nonparametric mapping (SnPM) and the Benjamini-Hochberg (BH) procedure for false discovery control. We applied these procedures to both six representative sports science datasets, and also to systematically varied simulated datasets which replicated ten signal- and/or noise-relevant parameters that were identified in the experimental datasets. We observed generally higher IWT and BH sensitivity for five of the six experimental datasets. BH was the most sensitive procedure in simulation, but also had relatively high false positive rates (generally > 0.1) which increased sharply (> 0.3) in certain extreme simulation scenarios including highly rough data. SPM and SnPM were more sensitive than IWT in simulation except for (1) high roughness, (2) high nonstationarity, and (3) highly nonuniform smoothness. These results suggest that the optimum procedure is both signal and noise-dependent. We conclude that: (1) BH is most sensitive but also susceptible to high false positive rates, (2) IWT, SPM and SnPM appear to have relatively inconsequential differences in terms of domain identification sensitivity, except in cases of extreme signal/noise characteristics, where IWT appears to be superior at identifying a greater portion of the true signal.

Ort, förlag, år, upplaga, sidor
Springer, 2023. Vol. 107, s. 369-392
Nyckelord [en]
One-dimensional functional data, Local inference, Continuum data analysis, Simulation, Signal modeling, Kinematics, Biomechanics
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-188526DOI: 10.1007/s10182-021-00418-4ISI: 000702599300001Scopus ID: 2-s2.0-85116225860OAI: oai:DiVA.org:umu-188526DiVA, id: diva2:1602303
Forskningsfinansiär
Vetenskapsrådet, 2016-02763Vetenskapsrådet, 2013-5203Tillgänglig från: 2021-10-12 Skapad: 2021-10-12 Senast uppdaterad: 2023-07-14Bibliografiskt granskad

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Abramowicz, KonradSjöstedt de Luna, SaraSchelin, Lina

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Pataky, Todd ColinAbramowicz, KonradPini, AlessiaSjöstedt de Luna, SaraSchelin, Lina
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Institutionen för matematik och matematisk statistikStatistik
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