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Hypothesis testing with active information

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  • Díaz–Pachón, Daniel Andrés
  • Sáenz, Juan Pablo
  • Rao, J. Sunil

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.

Suggested Citation

  • Díaz–Pachón, Daniel Andrés & Sáenz, Juan Pablo & Rao, J. Sunil, 2020. "Hypothesis testing with active information," Statistics & Probability Letters, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:stapro:v:161:y:2020:i:c:s0167715220300456
    DOI: 10.1016/j.spl.2020.108742
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    References listed on IDEAS

    as
    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    2. Daniel Andrés Díaz‐Pachón & Juan Pablo Sáenz & J. Sunil Rao & Jean‐Eudes Dazard, 2019. "Mode hunting through active information," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(2), pages 376-393, March.
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