Could Fisher, Jeffreys and Neyman Have Agreed on Testing?

By James O. Berger. 2003

365 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bfbcd0d4018f5755e25d65df3ac9ad3552542577

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Citations in the corpus

Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “Similar statements are found in Berger and Delampdy (1987), Falk and Greenbaum (1995), Goodman (2003), Hubbard and Bayari (2003), and throughout the Bayesian literature. The potency of these claims is sometimes reinforced by misrepresentation of what Bayesian methods themselves can provide. For exam”
Testing Fisher, Neyman, Pearson, and Bayes (2005, background): “Berger’s Fisher lecture attempted to create a consensus about testing; see Berger (2003).”
The need for Bayesian hypothesis testing in psychological science (2017, background): “For details on the confusion between the two paradigms see Berger (2003), Christensen (2005), Hubbard and Bayarri (2003).”
Testing hypotheses via a mixture estimation model (2014, background): “…of one, or comparing the posterior probability P(M1|x) to a bound derived from a 0–1 loss function (or a “golden” bound TESTING AS MIXTURE ESTIMATION 3 like α = 0.05 inspired from frequentist practice (Berger and Sellke, 1987; Berger et al., 1997, 1999; Berger, 2003; Ziliak and McCloskey, 2008).”
On the Jeffreys-Lindley Paradox (2014, methodology): “…ðfurtherÞ the prohibition of the use of improper priors for testing point-null hypotheses and selecting embedded models ðfound, e.g., in DeGroot 1982; Berger 1985; Robert 2001Þ. Depending on one’s perspective about the position of Bayesian statistics within statistical theories of inference, one…”
Bayesian inference in ecology (2004, background): “Efron (1978, 1986) and Dennis (1996, 2004) provide good overviews of the arguments for and against subjectivity and objectivity in Bayesian and frequentist inference; see Berger (2003) for an attempt at finding the middle ground.”
An Introduction to Second-Generation p-Values (2019, background): “Fisher would argue for the former and Neyman for the latter, but neither view has carried the day (Royall 1997; Berger 2003).”
Simple relation between Bayesian order-restricted and point-null hypothesis tests (2014, background): “Those who find the p value useful generally do not report Bayes factors, and those who report Bayes factors have no use for p values (but see Berger, 2003).”
A practical solution to the pervasive problems ofp values (2007, methodology): “The list of alternatives includes Bayesian procedures, Bayesian–frequentist compromises (see, e.g., Berger, 2003; Berger, Boukai, & Wang, 1997; Berger, Brown, & Wolpert, 1994; Good, 1983), Akaike’s information criterion (AIC; e.g., Akaike, 1974; Burnham & Anderson, 2002), cross-validation (e.g.,…”
Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing (2008, background): “As reported in a number of studies (e.g., Berger, 2003; Hubbard & Bayarri, 2003a; and especially Sellke et al., 2001), a simulation of the frequentist performance of p values is revealing.”
Information, Divergence and Risk for Binary Experiments (2009, background): “There are several works (Barnett, 1999; Bayarri and Berger, 2004; Berger, 2003) that compare and contrast these.”
Testing the significance of attribute interactions (2004, methodology): “Pearson’s goodness of fit testing which we primarily used in this paper is just one of possible testing protocols, and there has been much debate on this topic, e.g., (Berger, 2003).”