Bayesian Estimation of Log-Normal Means with Finite Quadratic Expected Loss (original) (raw)
December 2012 Bayesian Estimation of Log-Normal Means with Finite Quadratic Expected Loss
Enrico Fabrizi,Carlo Trivisano
Bayesian Anal. 7(4): 975-996 (December 2012). DOI: 10.1214/12-BA733
Abstract
The log-normal distribution is a popular model in biostatistics and other fields of statistics. Bayesian inference on the mean and median of the distribution is problematic because, for many popular choices of the prior for the variance (on the log-scale) parameter, the posterior distribution has no finite moments, leading to Bayes estimators with infinite expected loss for the most common choices of the loss function. We propose a generalized inverse Gaussian prior for the variance parameter, that leads to a log-generalized hyperbolic posterior, for which it is easy to calculate quantiles and moments, provided that they exist. We derive the constraints on the prior parameters that yield finite posterior moments of order r. We investigate the choice of prior parameters leading to Bayes estimators with optimal frequentist mean square error. For the estimation of the lognormal mean we show, using simulation, that the Bayes estimator under quadratic loss compares favorably in terms of frequentist mean square error to known estimators.
Citation
Enrico Fabrizi. Carlo Trivisano. "Bayesian Estimation of Log-Normal Means with Finite Quadratic Expected Loss." Bayesian Anal. 7 (4) 975 - 996, December 2012. https://doi.org/10.1214/12-BA733
Information
Published: December 2012
First available in Project Euclid: 27 November 2012
Digital Object Identifier: 10.1214/12-BA733
Keywords: Bayes estimators, Bessel functions, generalized hyperbolic distribution, generalized inverse gamma distribution
Rights: Copyright © 2012 International Society for Bayesian Analysis
Vol.7 • No. 4 • December 2012