Statistical Science

Discussion: Foundations of Statistical Inference, Revisited

Ryan Martin and Chuanhai Liu

Full-text: Open access

Abstract

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.

Article information

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

Dates
First available in Project Euclid: 18 August 2014

Permanent link to this document
https://projecteuclid.org/euclid.ss/1408368576

Digital Object Identifier
doi:10.1214/14-STS472

Mathematical Reviews number (MathSciNet)
MR3264537

Zentralblatt MATH identifier
1332.62024

Keywords
Birnbaum conditioning dimension reduction inferential model likelihood principle

Citation

Martin, Ryan; Liu, Chuanhai. Discussion: Foundations of Statistical Inference, Revisited. Statist. Sci. 29 (2014), no. 2, 247--251. doi:10.1214/14-STS472. https://projecteuclid.org/euclid.ss/1408368576


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See also

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