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Pretest and shrinkage estimators for log-normal means

Author

Listed:
  • Mahmoud Aldeni

    (Western Carolina University)

  • John Wagaman

    (Western Carolina University)

  • Mohamed Amezziane

    (Central Michigan University)

  • S. Ejaz Ahmed

    (Brock University)

Abstract

We consider the problem of pooling means from multiple random samples from log-normal populations. Under the homogeneity assumption of means that all mean values are equal, we propose improved large sample asymptotic methods for estimating p log-normal population means when multiple samples are combined. Accordingly, we suggest estimators based on linear shrinkage, pretest, and Stein-type methodology, and consider the asymptotic properties using asymptotic distributional bias and risk expressions. We also present a simulation study to validate the performance of the suggested estimators based on the simulated relative efficiency. Historical data from finance and weather are used to in the application of the proposed estimators.

Suggested Citation

  • Mahmoud Aldeni & John Wagaman & Mohamed Amezziane & S. Ejaz Ahmed, 2023. "Pretest and shrinkage estimators for log-normal means," Computational Statistics, Springer, vol. 38(3), pages 1555-1578, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-022-01286-5
    DOI: 10.1007/s00180-022-01286-5
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    References listed on IDEAS

    as
    1. Supranee Lisawadi & S. Ejaz Ahmed & Orawan Reangsephet & Muhammad Kashif Ali Shah, 2019. "Simultaneous estimation of Cronbach’s alpha coefficients," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(13), pages 3236-3257, July.
    2. Samadrita Bera & Nabakumar Jana, 2022. "On estimating common mean of several inverse Gaussian distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 115-139, January.
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