Asymptotically Informative Prior for Bayesian Analysis
AbstractIn classical Bayesian inference the prior is treated as fixed, it is asymptotically negligible,thus any information contained in the prior is ignored from the asymptotic first order result.However, in practice often an informative prior is summarized from previous similar or the samekind of studies, which contains non-negligible information for the current study. Here, differentfrom traditional Bayesian point of view, we treat such prior to be non-fixed. In particular,we give the data sizes used in previous studies for the prior the same status as the size of thecurrent dataset, viewing both sample sizes as increasing to infinity in the asymptotic study.Thus the prior is asymptotically non-negligible, and its original effects are ressumed under thisview. Consequently, Bayesian inference using such prior is more efficient, as it should be, thanthat regarded under the existing setting. We study some basic properties of Bayesian estimatorsusing such priors under convex losses and the 0—1 loss, and illustrate the method by an examplevia simulation.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-130/4.
Date of creation: 15 Sep 2011
Date of revision:
Contact details of provider:
Web page: http://www.tinbergen.nl
Asymptotically informative prior; asymptotic efficiency; Bayes estimator; information bound; maximum likelihood estimator;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bradley Efron, 2005. "Bayesians, Frequentists, and Scientists," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1-5, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Antoine Maartens (+31 626 - 160 892)).
If references are entirely missing, you can add them using this form.