TY  - JOUR
A1  - Ditzhaus, Marc
A1  - Gaigall, Daniel
T1  - A consistent goodness-of-fit test for huge dimensional and functional data
T2  - Journal of Nonparametric Statistics
N2  - A nonparametric goodness-of-fit test for random variables with values in a separable Hilbert space is investigated. To verify the null hypothesis that the data come from a specific distribution, an integral type test based on a Cramér-von-Mises statistic is suggested. The convergence in distribution of the test statistic under the null hypothesis is proved and the test's consistency is concluded. Moreover, properties under local alternatives are discussed. Applications are given for data of huge but finite dimension and for functional data in infinite dimensional spaces. A general approach enables the treatment of incomplete data. In simulation studies the test competes with alternative proposals.
KW  - Cramér-von-Mises statistic
KW  - separable Hilbert space
KW  - huge dimensional data
KW  - functional data
Y1  - 2018
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/10414
SN  - 1029-0311
VL  - 30
IS  - 4
SP  - 834
EP  - 859
PB  - Taylor & Francis
CY  - Abingdon
ER  -