Hypothesis testing with active information

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number108742
JournalStatistics and Probability Letters
Volume161
DOIs
StatePublished - Jun 2020

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Keywords

  • Active information
  • Exact p-values
  • Hypothesis testing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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