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@article{1534363,
author = {Kraus, David},
article_location = {San Diego},
article_number = {September},
doi = {http://dx.doi.org/10.1016/j.jmva.2019.05.002},
keywords = {Bootstrap; Covariance operator; Functional data; K-sample test; Partial observation; Principal components},
language = {eng},
issn = {0047-259X},
journal = {Journal of Multivariate Analysis},
title = {Inferential procedures for partially observed functional data},
url = {https://www.sciencedirect.com/science/article/pii/S0047259X18304950},
volume = {173},
year = {2019}
}
TY - JOUR
ID - 1534363
AU - Kraus, David
PY - 2019
TI - Inferential procedures for partially observed functional data
JF - Journal of Multivariate Analysis
VL - 173
IS - September
SP - 583-603
EP - 583-603
PB - Elsevier
SN - 0047259X
KW - Bootstrap
KW - Covariance operator
KW - Functional data
KW - K-sample test
KW - Partial observation
KW - Principal components
UR - https://www.sciencedirect.com/science/article/pii/S0047259X18304950
L2 - https://www.sciencedirect.com/science/article/pii/S0047259X18304950
N2 - 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.
ER -
KRAUS, David. Inferential procedures for partially observed functional data. Online. \textit{Journal of Multivariate Analysis}. San Diego: Elsevier, 2019, vol.~173, September, p.~583-603. ISSN~0047-259X. Available from: https://dx.doi.org/10.1016/j.jmva.2019.05.002. [citováno 2024-04-23]
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