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

Document Type : Original Scientific Paper

Authors

Department of Statistics‎, ‎University of Mazandaran‎, ‎‎Babolsar‎, ‎Iran

Abstract

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.

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References

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Journal of Statistical Modelling: Theory and Applications
Volume 6, Issue 1
January 2025
Pages 167-183

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History

  • Receive Date: 04 May 2025
  • Revise Date: 10 November 2025
  • Accept Date: 06 January 2026

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