bayesian prediction for the exponential distribution under the exponential squared error loss function

A recently proposed asymmetric loss function, termed the exponential squared error loss function, has been applied to estimation problems. however, prediction methodologies based on this loss function remain unexplored in the literature. in this paper, we focus on bayesian two-sample prediction for the exponential distribution under this new loss function. we consider situations where the informative sample size may be either fixed or random. since the bayesian predictors do not have closed forms, we employ a monte carlo method to approximate them. a simulation study is conducted to evaluate the performance of the predictors. the simulation results indicate that predictors with fixed sample sizes can outperform those with truncated poisson and truncated geometrically distributed sample sizes. a real data example is also presented for illustration. the paper concludes with a discussion.