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Efficiency of Conformal Testing

In: Algorithmic Learning in a Random World

Author

Listed:
  • Vladimir Vovk

    (University of London, Royal Holloway)

  • Alexander Gammerman

    (University of London, Royal Holloway)

  • Glenn Shafer

    (Rutgers University)

Abstract

Our question in this chapter is how much we can potentially lose when relying on conformal test martingales as compared with unrestricted testing of either exchangeability or statistical randomness. We will see that at a crude scale we do not lose much when restricting our attention to testing statistical randomness with conformal test martingales. Following the old tradition of the algorithmic theory of randomness, in this chapter we consider the binary case only; this makes our results less relevant in practical applications, but they are still reassuring. We consider two very different settings of the problem. In the first, we are given an arbitrary critical region for testing exchangeability of finite binary sequences of a given length, and the question is whether there exists a conformal test martingale emulating this critical region. In the second, we are given a natural alternative, simple or composite, to the hypothesis of exchangeability, and the question is whether there exists a conformal test martingale competitive with the likelihood ratio of the alternative to the null hypothesis (suitably defined).

Suggested Citation

  • Vladimir Vovk & Alexander Gammerman & Glenn Shafer, 2022. "Efficiency of Conformal Testing," Springer Books, in: Algorithmic Learning in a Random World, edition 2, chapter 0, pages 265-304, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-06649-8_9
    DOI: 10.1007/978-3-031-06649-8_9
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