Pretest and shrinkage estimators for log-normal means

Mahmoud Aldeni, John Wagaman, Mohamed Amezziane, S. Ejaz Ahmed

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
JournalComputational Statistics
DOIs
StateAccepted/In press - 2022

Keywords

  • Asymptotic bias and risk
  • Homogeneity
  • Pretest estimators
  • Stein-type estimators

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