Performances of several methods currently used for detection of discordant observations are reviewed, considering a set of absolute measurements of gravity acceleration exhibiting some peculiar features. Along with currently used methods, a criterion based upon distribution of extremes is also relied upon to provide references; a modification of a simple, broadly used method is mentioned, improving performances while retaining inherent ease of use. Identification of distributions underlying experimental data may entail a substantial uncertainty component, particularly when sample size is small, and no mechanistic models are available. A pragmatic approach is described, providing estimation to a first approximation of overall uncertainty, covering both estimation of parameters, and identification of distribution shape.

Treatment of Experimental Data with Discordant Observations: Issues in Empirical Identification of Distribution / Barbato, G.; Genta, G.; Germak, ALESSANDRO FRANCO LIDIA; Levi, R.; Vicario, G.. - In: MEASUREMENT SCIENCE REVIEW. - ISSN 1335-8871. - 12:4(2012), pp. 133-140. [10.2478/v10048-012-0020-y]

Treatment of Experimental Data with Discordant Observations: Issues in Empirical Identification of Distribution

GERMAK, ALESSANDRO FRANCO LIDIA;
2012

Abstract

Performances of several methods currently used for detection of discordant observations are reviewed, considering a set of absolute measurements of gravity acceleration exhibiting some peculiar features. Along with currently used methods, a criterion based upon distribution of extremes is also relied upon to provide references; a modification of a simple, broadly used method is mentioned, improving performances while retaining inherent ease of use. Identification of distributions underlying experimental data may entail a substantial uncertainty component, particularly when sample size is small, and no mechanistic models are available. A pragmatic approach is described, providing estimation to a first approximation of overall uncertainty, covering both estimation of parameters, and identification of distribution shape.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11696/33400
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