 Default priors for Bayesian and frequentist inferenceD. A. S. Fraser, N. Reid, E. Marras and G. Y. Yi Journal of the Royal Statistical Society Series B, 2010, vol. 72, issue 5, 631-654 Abstract: Summary. We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inference. Such a prior is a density or relative density that weights an observed likelihood function, leading to the elimination of parameters that are not of interest and then a density‐type assessment for a parameter of interest. For independent responses from a continuous model, we develop a prior for the full parameter that is closely linked to the original Bayes approach and provides an extension of the right invariant measure to general contexts. We then develop a modified prior that is targeted on a component parameter of interest and by targeting avoids the marginalization paradoxes of Dawid and co‐workers. This modifies Jeffreys's prior and provides extensions to the development of Welch and Peers. These two approaches are combined to explore priors for a vector parameter of interest in the presence of a vector nuisance parameter. Examples are given to illustrate the computation of the priors. Date: 2010 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:72:y:2010:i:5:p:631-654 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC. |
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