Abstract
We propose a new nonparametric test for component independence which is based on application of data compressors to ranked data. For two-component data sample the idea is to break the sample in two parts and permute one of the components in the second part, while leaving the first part intact. The resulting two samples are then jointly ranked and a data compressor is applied to the resulting (binary) data string. The components are deemed independent if the string cannot be compressed. This procedure gives a provably valid test against all possible alternatives (that is, the test is distribution-free) provided the data compressor was ideal.
This research was supported by the Swiss NSF grants 200020-107616 and 200021-113364.
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Ryabko, D. (2007). Testing Component Independence Using Data Compressors. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_83
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DOI: https://doi.org/10.1007/978-3-540-74695-9_83
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