What does fiducial mean (in the context of statistics)? 26 When I Google for "fisher" "fiducial" I sure get a lot of hits, but all the ones I've followed are utterly beyond my comprehension All these hits do seem to have one thing in common: they are all written for dyed-in-the-wool statisticians, people thoroughly steeped in the theory, practice, history, and lore of statistics
What is the fiducial argument and why has it not been accepted? One of the late contributions of R A Fisher was fiducial intervals and fiducial principled arguments This approach however is nowhere near as popular as frequentist or Bayesian principled argumen
Fiducial Inference in Machine Learning - Cross Validated I was looking at the Fiducial Inference page on wikipedia, which is an alternative to the traditional Frequentist and Bayesian standpoints Although it was out of favour in mainstream statistics fo
Compare 90th percentiles of two samples (confidence interval, test) Interpret the fiducial distributions as probabilitiy distributions (this is an approximation, the fiducial distribution does not behave exactly like a probability distribution) and compute the probabilities for the joint probabilities of the two parameters percentiles to be inside the 2-d bins cells created by the grid
Understanding the Behrens–Fisher problem - Cross Validated That is what led to fiducial inference A Link to the Savage article The Biography by Fisher's daughter Joan Fisher Box R A Fisher An Appreciation, Hinkley and Feinberg editors A book by Erich Lehmann about Fisher and Neyman and the birth of Classical Statistics This is a link to an earlier post that I commented on that you also posted
inference - Behrens–Fisher problem - Cross Validated This problem was the one that exposed the difference between fiducial inference and the Neyman-Pearson hypothesis testing approach Prior to that Fisher recognized philosophical differences but thought that the two methods gave the same answers
Doing maximum p-value estimation instead of maximum likelihood This isn't exactly what you're asking, but you may be interested in reading about "Fiducial Inference" (This really answers the question "How can we turn a p-value into a confidence interval" rather than your question "How can we turn a p-value into a point estimate", but it may still be of interest)
p-values for hypothesis testing - Cross Validated The reason for using those 'irrelevant' values is because fiducial probability, p-values, and confidence intervals answer a different question, or at least use a different approach (they condition on the parameters instead of on the observation) See the question and answer here: An example where the likelihood principle really matters?