The uncertainty reduction for the refined sample mean of combined quantities

Wen Hui Lo*, Sin-Horng Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Sampling plans for middle or small sample size are often taken when the available data is not large enough. In this paper, a new quantile-based maximum likelihood estimation (QMLE) method for mean value estimation of a quasi-normal distribution is proposed. It takes the two endpoints of range as quasi-symmetric quantiles and fuses the concept of empirical and symmetric quantiles to define an objective function. It then follows the well-known "asymptotic minimax principle", which is a robust statistical method, to realize the optimization of the objective function. Simulation results confirmed that the proposed QMLE mean estimator outperforms the conventional sample mean estimator with about 40% uncertainty, or mean square error (MSE), reduction.

Original languageEnglish
Number of pages6
JournalIAENG International Journal of Applied Mathematics
Volume39
Issue number3
StatePublished - 1 Aug 2009

Keywords

  • Asymptotic minimax principle
  • Combined quantities
  • Maximum likelihood estimation
  • Quantile
  • Sample mean

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