Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Simultaneous inference for functional data in sports biomechanics: Comparing statistical parametric mapping with interval-wise testing
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Show others and affiliations
2023 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818X, Vol. 107, p. 369-392Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 107, p. 369-392
Keywords [en]
One-dimensional functional data, Local inference, Continuum data analysis, Simulation, Signal modeling, Kinematics, Biomechanics
National Category
Probability Theory and Statistics
Identifiers
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
Funder
Swedish Research Council, 2016-02763Swedish Research Council, 2013-5203Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2023-07-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Abramowicz, KonradSjöstedt de Luna, SaraSchelin, Lina

Search in DiVA

By author/editor
Pataky, Todd ColinAbramowicz, KonradPini, AlessiaSjöstedt de Luna, SaraSchelin, Lina
By organisation
Department of Mathematics and Mathematical StatisticsStatistics
In the same journal
AStA Advances in Statistical Analysis
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 264 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf