Pretest and shrinkage estimators for log-normal means

Mahmoud Aldeni (), John Wagaman (), Mohamed Amezziane () and S. Ejaz Ahmed ()
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Mahmoud Aldeni: Western Carolina University
John Wagaman: Western Carolina University
Mohamed Amezziane: Central Michigan University
S. Ejaz Ahmed: Brock University

Computational Statistics, 2023, vol. 38, issue 3, No 20, 1555-1578

Abstract: 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.

Keywords: Homogeneity; Pretest estimators; Stein-type estimators; Asymptotic bias and risk (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01286-5

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