The lognormal distribution, LogN(ξ,σ²), is a popular model in many fields of statistics. The mean, the mode, the median and other summaries of the lognormal may be written as special cases of θ_a,b = exp(aξ + bσ²) for different choices of a, b. Bayesian inference on θ_a,b under quadratic loss is problematic since, for many popular choices of the prior for σ², it has no finite moments. In this paper we propose a Generalized Inverse Gaussian prior for σ² that leads to a log-Generalized Hyperbolic posterior for θ_a,b, a distribution for which it is easy to calculate quantiles and moments, provided that they exist. We derive the constraints on the prior parameters that yields finite posterior moments of order r and closed form expressions for the posterior mean and variance of θ_a,b. We investigate the choice of prior parameters leading to a posterior mean with optimal frequentist MSE using a second-order 'small argument' approximation to the modified Bessel functions of the third kind. We also explore prior specification when the hypotheses on which the 'small argument' approximation is based do not hold.

The relative quadratic loss function has often been used in the literature to summarize the posterior for θ_a,b. We show that, under our approach, also the Bayes estimator obtained minimizing the relative quadratic loss may be written in closed form.

With reference to the estimation of the lognromal mean we show that popular estimators may be obtained as special cases of our posterior mean; we also show, by simulation, that our posterior mean compares favourably to known estimators of the lognromal mean in terms of frequentist MSE. In particular it is more efficient than the Bayes estimator obtained minimizing the relative quadratic loss.

Keywords: logNormal distribution; Bessel functions; generalized inverse gaussian distribution; generalized hyperbolic distribution

Biography: Enrico Fabrizi is a researcher in Statistics at the Catholic University of the Sacred Heart at Piacenza, Italy. He holds a PhD in Statistics from the Department of Statistics, University of Bologna. He has research interests in survey sampling methodology, Bayesian inference applied to the analysis of complex survey data, small area estimation. He is also interested in estimation of welfare and poverty related parameters at the sub-national level.