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Ventura, Laura and Racugno, Walter (2010) Recent advances on Bayesian inference for P(X min Y ). [Working Paper] WORKING PAPER SERIES, 7/2010 . , PADOVA

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Abstract (english)

We address the statistical problem of evaluating R = P(X < Y ), where X and Y are two independent random variables. Bayesian parametric inference about R, based on the marginal posterior density of R, has been widely discussed under various distributional assumptions on X and Y . This classical approach requires both elicitation of a prior on the complete parameter and numerical integration in order to derive the marginal distribution of R. In this paper, we discuss and apply recent advances in Bayesian inference based on higher-order asymptotics and on pseudo-likelihoods, and related matching priors, which allow to perform accurate inference on the parameter of interest only. The proposed approach has the advantages of avoiding the elicitation on the nuisance parameters and the computation of multidimensional integrals.
The accuracy of the proposed methodology is illustrated both by numerical studies and by real-life data concerning clinical studies

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EPrint type:Working Paper
Anno di Pubblicazione:June 2010
More information:Pubblicato anche in: Bayesian Analysis, Volume 6, Number 3 (2011), 411-428; http://projecteuclid.org/euclid.ba/1339616470; doi:10.1214/ba/1339616470
Key Words:Asymptotic expansions, Frequentist coverage probability, Matching prior, Modified likelihood root, Modified profile likelihood, Nuisance parameter, ROC curve, Stochastic precedence, Stress-strength model, Tail area probability
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:8827
Depositato il:10 Jun 2015 13:30
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