Estimation of a normal process variance from measurements with large round-off errors

Benson Karhi Diamanta, Dvir Ellite, Regev Itai, Schechtman Edna

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

Abstract

Measurements are sometimes affected by excessively large round-off errors. Small rounding-off may safely be ignored for purposes of statistical inference however large rounding-off may have an effect. The importance of the round-off is determined by the ratio between the standard deviation σ and the instrument's scale step h, δ =σ /h. In this paper we estimate σ when δ is small (δ < 0.5) using a variant of the Method of Moments (MoM). The MoM estimators are compared with the Maximum Likelihood Estimators (MLE), using simulation. We find that the MoM can improve the estimation in terms of MSE and bias, especially under circumstances where the MLE method is not accurate or cannot provide a solution.

Original languageEnglish
Title of host publicationRecent Advances in Manufacturing Engineering - Proceedings of the 4th International Conference on Manufacturing Engineering, Quality and Production Systems, MEQAPS'11
Pages297-302
Number of pages6
StatePublished - 2011
Event4th International Conference on Manufacturing Engineering, Quality and Production Systems, MEQAPS'11 - Barcelona, Spain
Duration: 15 Sep 201117 Sep 2011

Publication series

NameInternational Conference on Manufacturing Engineering, Quality and Production Systems, MEQAPS - Proceedings
ISSN (Print)1792-4693

Conference

Conference4th International Conference on Manufacturing Engineering, Quality and Production Systems, MEQAPS'11
Country/TerritorySpain
CityBarcelona
Period15/09/1117/09/11

Keywords

  • Control chart
  • Curve fitting
  • Level of quantization
  • Measurement error
  • Method of moments
  • Roundoff error
  • Statistical process control

Fingerprint

Dive into the research topics of 'Estimation of a normal process variance from measurements with large round-off errors'. Together they form a unique fingerprint.

Cite this