Journal Article

Hypothesis testing with active information

Statistics and Probability Letters (2020) 161
DOI: 10.1016/j.spl.2020.108742
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Abstract

We develop hypothesis testing for active information — the averaged quantity in the Kullback–Leibler divergence. To our knowledge, this is the first paper to derive exact probabilities of type-I errors for hypothesis testing in the area.

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Díaz-Pachón, D. A., Sáenz, J. P., & Rao, J. S. (2020). Hypothesis testing with active information. Statistics and Probability Letters, 161. https://doi.org/10.1016/j.spl.2020.108742

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