Discussion: Foundations of Statistical Inference, Revisited
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. [arXiv:1302.7021]
Keywords
Cite
@article{arxiv.1312.7183,
title = {Discussion: Foundations of Statistical Inference, Revisited},
author = {Ryan Martin and Chuanhai Liu},
journal= {arXiv preprint arXiv:1312.7183},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/14-STS472 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)