The mean estimation of the combined quantities by the asymptotic minimax optimization

Wen Hui Lo*, Sin-Horng Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The mean value estimation for the output quantity of combined random variables is one of the major issues in measurement. In this paper, a new quantile-based maximum likelihood estimation (QMLE) method for mean value estimation is proposed. It fuses the concept of both empirical and symmetric quantile to incorporate the order statistics into the QMLE. Unlike Sample mean derived basing only on the maximum likelihood criterion, the QMLE also considers MMSE defined using the quasi symmetric quantiles (QSQ), i.e., the first- and last-order samples. Simulation results confirm that the proposed QMLE mean estimator outperforms the conventional Sample mean estimator. This work also gives a looking-up table for the refinement corresponding to the QSQ adjustments.

Original languageEnglish
Title of host publication2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, AMUEM 2009
Pages63-68
Number of pages6
DOIs
StatePublished - 18 Nov 2009
Event2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, AMUEM 2009 - Bucharest, Romania
Duration: 6 Jul 20097 Jul 2009

Publication series

Name2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, AMUEM 2009

Conference

Conference2009 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement, AMUEM 2009
CountryRomania
CityBucharest
Period6/07/097/07/09

Keywords

  • Central limit theorem
  • Combined quantities
  • Maximum likelihood estimation
  • Quantile
  • Sample mean

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