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A Kruskal-Wallis type test for functional data.

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Τίτλος: A Kruskal-Wallis type test for functional data.
Συγγραφείς: Rodriguez, Daniela1 (AUTHOR), Sued, Mariela2 (AUTHOR), Valdora, Marina3 (AUTHOR) mvaldora@dm.uba.ar
Πηγή: Communications in Statistics: Simulation & Computation. 2026, Vol. 55 Issue 5, p1787-1801. 15p.
Θεματικοί όροι: *Functional analysis, *Statistical hypothesis testing, Kruskal-Wallis Test, Robust statistics, Random projection method, False discovery rate
Περίληψη: We present a non-parametric method to decide whether K independent samples of functional data have the same location parameter. To do so, random projections are used to transform functional data into univariate data and a Kruskal Wallis test is then applied in order to solve the one-dimensional problem. The p-values obtained from different projections are combined by applying the False Discovery Rate correction, giving rise to a new procedure that solves the functional problem. The method is explored through synthetic data and analyzing two real data sets. [ABSTRACT FROM AUTHOR]
Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Kruskal-Wallis type test for functional data.
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  Data: <searchLink fieldCode="AR" term="%22Rodriguez%2C+Daniela%22">Rodriguez, Daniela</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sued%2C+Mariela%22">Sued, Mariela</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Valdora%2C+Marina%22">Valdora, Marina</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> mvaldora@dm.uba.ar</i>
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  Data: <searchLink fieldCode="JN" term="%22Communications+in+Statistics%3A+Simulation+%26+Computation%22">Communications in Statistics: Simulation & Computation</searchLink>. 2026, Vol. 55 Issue 5, p1787-1801. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Functional+analysis%22">Functional analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22Kruskal-Wallis+Test%22">Kruskal-Wallis Test</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Random+projection+method%22">Random projection method</searchLink><br /><searchLink fieldCode="DE" term="%22False+discovery+rate%22">False discovery rate</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We present a non-parametric method to decide whether K independent samples of functional data have the same location parameter. To do so, random projections are used to transform functional data into univariate data and a Kruskal Wallis test is then applied in order to solve the one-dimensional problem. The p-values obtained from different projections are combined by applying the False Discovery Rate correction, giving rise to a new procedure that solves the functional problem. The method is explored through synthetic data and analyzing two real data sets. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/03610918.2025.2455418
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: 1787
    Subjects:
      – SubjectFull: Functional analysis
        Type: general
      – SubjectFull: Statistical hypothesis testing
        Type: general
      – SubjectFull: Kruskal-Wallis Test
        Type: general
      – SubjectFull: Robust statistics
        Type: general
      – SubjectFull: Random projection method
        Type: general
      – SubjectFull: False discovery rate
        Type: general
    Titles:
      – TitleFull: A Kruskal-Wallis type test for functional data.
        Type: main
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          Name:
            NameFull: Rodriguez, Daniela
      – PersonEntity:
          Name:
            NameFull: Sued, Mariela
      – PersonEntity:
          Name:
            NameFull: Valdora, Marina
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          Dates:
            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
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              Value: 03610918
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              Value: 55
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Communications in Statistics: Simulation & Computation
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