• Corpus ID: 247594613

Exact Anytime-valid Confidence Intervals for Contingency Tables and Beyond

  title={Exact Anytime-valid Confidence Intervals for Contingency Tables and Beyond},
  author={Rosanne Turner and Peter D. Grunwald},
E-variables are tools for retaining type-I error guarantee with optional stopping. We extend E-variables for sequential two-sample tests to general null hypotheses and anytime-valid confidence sequences. We provide implementations for estimating risk difference, relative risk and odds-ratios in contingency tables. 

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  • Mathematics
    Proceedings of the National Academy of Sciences of the United States of America
  • 1967
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