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@inproceedings{Laumann:2021:10.3390/engproc2021005031,
author = {Laumann, F and von, Kuegelgen J and Barahona, M},
doi = {10.3390/engproc2021005031},
pages = {1--13},
publisher = {https://www.mdpi.com/2673-4591/5/1/31},
title = {Kernel two-sample and independence tests for non-stationary random processes},
url = {http://dx.doi.org/10.3390/engproc2021005031},
year = {2021}
}
TY - CPAPER
AB - Two-sample and independence tests with the kernel-based MMD and HSIC haveshown remarkable results on i.i.d. data and stationary random processes.However, these statistics are not directly applicable to non-stationary randomprocesses, a prevalent form of data in many scientific disciplines. In thiswork, we extend the application of MMD and HSIC to non-stationary settings byassuming access to independent realisations of the underlying random process.These realisations - in the form of non-stationary time-series measured on thesame temporal grid - can then be viewed as i.i.d. samples from a multivariateprobability distribution, to which MMD and HSIC can be applied. We further showhow to choose suitable kernels over these high-dimensional spaces by maximisingthe estimated test power with respect to the kernel hyper-parameters. Inexperiments on synthetic data, we demonstrate superior performance of ourproposed approaches in terms of test power when compared to currentstate-of-the-art functional or multivariate two-sample and independence tests.Finally, we employ our methods on a real socio-economic dataset as an exampleapplication.
AU - Laumann,F
AU - von,Kuegelgen J
AU - Barahona,M
DO - 10.3390/engproc2021005031
EP - 13
PB - https://www.mdpi.com/2673-4591/5/1/31
PY - 2021///
SP - 1
TI - Kernel two-sample and independence tests for non-stationary random processes
UR - http://dx.doi.org/10.3390/engproc2021005031
UR - http://arxiv.org/abs/2010.00271v3
UR - https://www.mdpi.com/2673-4591/5/1/31
UR - http://hdl.handle.net/10044/1/90848
ER -