Default priors for Bayesian and frequentist inference

By Donald Fraser, Nancy Reid, Elisabetta Marras, Grace Y. Yi. 2010

78 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/94cf5196054b4613f32d656c2f325d7a947ed6cd

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

A note on p-values interpreted as plausibilities (2012, background): “nﬁdence intervals (Chiang 2001; Tsui and Weerahandi 1989; Weerahandi 1993), and Bayesian inference with default, reference, and/or data-dependent priors (Berger 2006; Berger et al. 2009; Fraser 2011; Fraser et al. 2010; Ghosh 2011). IMs, ﬁducial, and Dempster–Shafer theory all introduce auxiliary va”
On Some Principles of Statistical Inference (2015, background): “The extensive development of reference priors and other forms of priors meant to be uninformative with respect to the parameters clearly indicates that such priors must be targeted to the parameters of interest (Berger & Bernardo, 1992; Berger et al., 2009; Fraser et al., 2010).”
Generalized Fiducial Inference: A Review and New Results (2016, background): “Another related approach is based on higher order likelihood expansions and implied data-dependent priors (Fraser 2004, 2011; Fraser, Reid, and Wong 2005; Fraser and Naderi 2008; Fraser, Fraser, and Staicu 2009; Fraser et al. 2010).”
Generalized fiducial inference via discretization (2013, background): “…we remark that there are interesting connections between generalized fiducial distribution, the asymptotic theory of likelihood (Davison et al., 2006), the theory of second order ancillaries (Fraser et al., 2010a) and as discussed in Section 3, the data dependent prior of Fraser et al. (2010b).”
Is Bayes Posterior just Quick and Dirty Confidence (2011, background): “Of course, data dependent priors have a long but rather infrequent presence, for example, Box and Cox (1964), Wasserman (2000) and Fraser et al. (2010b).”
Calibrating general posterior credible regions (2015, methodology): “Bayesian and Gibbs posterior procedure, as well as variational Bayes, as we demonstrate in Section 4. It also covers classical empirical Bayes or other posteriors based on data-dependent priors (e.g. Fraser et al. 2010; Hannig et al. 2016; Martin and Walker 2016). The one technical requirement we ha”