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arXiv · 1607.07270

A Statistical Test for Joint Distributions Equivalence

Francesco Solera·Andrea Palazzi
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Abstract

We provide a distribution-free test that can be used to determine whether any two joint distributions $p$ and $q$ are statistically different by inspection of a large enough set of samples. Following recent efforts from Long et al. [1], we rely on joint kernel distribution embedding to extend the kernel two-sample test of Gretton et al. [2] to the case of joint probability distributions. Our main result can be directly applied to verify if a dataset-shift has occurred between training and test distributions in a learning framework, without further assuming the shift has occurred only in the input, in the target or in the conditional distribution.

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Francesco Solera, Andrea Palazzi. 2016-07-25. A Statistical Test for Joint Distributions Equivalence. https://arxiv.org/abs/1607.07270

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