2019
Inferential procedures for partially observed functional data
KRAUS, DavidZákladní údaje
Originální název
Inferential procedures for partially observed functional data
Autoři
KRAUS, David (203 Česká republika, garant, domácí)
Vydání
Journal of Multivariate Analysis, San Diego, Elsevier, 2019, 0047-259X
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10103 Statistics and probability
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 1.136
Kód RIV
RIV/00216224:14310/19:00107420
Organizační jednotka
Přírodovědecká fakulta
UT WoS
000481565500034
EID Scopus
2-s2.0-85066395567
Klíčová slova anglicky
Bootstrap; Covariance operator; Functional data; K-sample test; Partial observation; Principal components
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 18. 3. 2020 14:07, Mgr. Marie Novosadová Šípková, DiS.
Anotace
V originále
In functional data analysis it is usually assumed that all functions are completely, densely or sparsely observed on the same domain. Recent applications have brought attention to situations where each functional variable may be observed only on a subset of the domain while no information about the function is available on the complement. Various advanced methods for such partially observed functional data have already been developed but, interestingly, some essential methods, such as K-sample tests of equal means or covariances and confidence intervals for eigenvalues and eigenfunctions, are lacking. Without requiring any complete curves in the data, we derive asymptotic distributions of estimators of the mean function, covariance operator and eigenelements and construct hypothesis tests and confidence intervals. To overcome practical difficulties with storing large objects in computer memory, which arise due to partial observation, we use the nonparametric bootstrap approach. The proposed methods are investigated theoretically, in simulations and on a fragmentary functional data set from medical research.
Návaznosti
GJ17-22950Y, projekt VaV |
|