Statistical Science

Discussion: Foundations of Statistical Inference, Revisited

Ryan Martin and Chuanhai Liu

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This is an invited contribution to the discussion on Professor Deborah Mayo’s paper, “On the Birnbaum argument for the strong likelihood principle,” to appear in Statistical Science. Mayo clearly demonstrates that statistical methods violating the likelihood principle need not violate either the sufficiency or conditionality principle, thus refuting Birnbaum’s claim. With the constraints of Birnbaum’s theorem lifted, we revisit the foundations of statistical inference, focusing on some new foundational principles, the inferential model framework, and connections with sufficiency and conditioning.

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Statist. Sci., Volume 29, Number 2 (2014), 247-251.

First available in Project Euclid: 18 August 2014

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Birnbaum conditioning dimension reduction inferential model likelihood principle


Martin, Ryan; Liu, Chuanhai. Discussion: Foundations of Statistical Inference, Revisited. Statist. Sci. 29 (2014), no. 2, 247--251. doi:10.1214/14-STS472.

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See also

  • Main article: On the Birnbaum Argument for the Strong Likelihood Principle.