Testing homogeneity: the trouble with sparse functional data.
convergence rate
energy distance
longitudinal data
measurement errors
sparse functional data
two-sample test
Journal
Journal of the Royal Statistical Society. Series B, Statistical methodology
ISSN: 1467-9868
Titre abrégé: J R Stat Soc Series B Stat Methodol
Pays: England
ID NLM: 100890344
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
02
07
2022
revised:
06
12
2022
accepted:
25
02
2023
medline:
31
7
2023
pubmed:
31
7
2023
entrez:
31
7
2023
Statut:
epublish
Résumé
Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be done for such data. In particular, we show that testing the marginal homogeneity based on point-wise distributions is feasible under some mild constraints and propose a new two-sample statistic that works well with both intensively and sparsely measured functional data. The proposed test statistic is formulated upon energy distance, and the convergence rate of the test statistic to its population version is derived along with the consistency of the associated permutation test. The aptness of our method is demonstrated on both synthetic and real data sets.
Identifiants
pubmed: 37521166
doi: 10.1093/jrsssb/qkad021
pii: qkad021
pmc: PMC10376451
doi:
Types de publication
Journal Article
Langues
eng
Pagination
705-731Informations de copyright
© (RSS) Royal Statistical Society 2023.
Déclaration de conflit d'intérêts
Conflict of interest: We have no conflicts of interest to disclose.
Références
J Stat Plan Inference. 2015 Jan;156:1-13
pubmed: 26023253
Aust N Z J Stat. 2018 Mar;60(1):4-19
pubmed: 30197552
J Am Stat Assoc. 2022;117(537):348-360
pubmed: 35757778
Biometrics. 2016 Sep;72(3):835-45
pubmed: 26811864
J R Stat Soc Ser C Appl Stat. 2016 Apr 1;65(3):395-414
pubmed: 27041772