Conformity assessment of the distribution of the values of a quantity is investigated by using a Bayesian approach. The effect of systematic, non-negligible measurement errors is taken into account. The analysis is general, in the sense that the probability distribution of the quantity can be of any kind, that is even different from the ubiquitous normal distribution, and the measurement model function, linking the measurand with the observable and non-observable influence quantities, can be non-linear. Further, any joint probability density function can be used to model the available knowledge about the systematic errors. It is demonstrated that the result of the Bayesian analysis here developed reduces to the standard result (obtained through a frequentistic approach) when the systematic measurement errors are negligible. A consolidated frequentistic extension of such standard result, aimed at including the effect of a systematic measurement error, is directly compared with the Bayesian result, whose superiority is demonstrated. Application of the results here obtained to the derivation of the operating characteristic curves used for sampling plans for inspection by variables is also introduced

Bayesian conformity assessment in presence of systematic measurement errors / Carobbi, Carlo; Pennecchi, FRANCESCA ROMANA. - In: METROLOGIA. - ISSN 0026-1394. - 53:2(2016), pp. S74-S80. [10.1088/0026-1394/53/2/S74]

Bayesian conformity assessment in presence of systematic measurement errors

PENNECCHI, FRANCESCA ROMANA
2016

Abstract

Conformity assessment of the distribution of the values of a quantity is investigated by using a Bayesian approach. The effect of systematic, non-negligible measurement errors is taken into account. The analysis is general, in the sense that the probability distribution of the quantity can be of any kind, that is even different from the ubiquitous normal distribution, and the measurement model function, linking the measurand with the observable and non-observable influence quantities, can be non-linear. Further, any joint probability density function can be used to model the available knowledge about the systematic errors. It is demonstrated that the result of the Bayesian analysis here developed reduces to the standard result (obtained through a frequentistic approach) when the systematic measurement errors are negligible. A consolidated frequentistic extension of such standard result, aimed at including the effect of a systematic measurement error, is directly compared with the Bayesian result, whose superiority is demonstrated. Application of the results here obtained to the derivation of the operating characteristic curves used for sampling plans for inspection by variables is also introduced
File in questo prodotto:
File Dimensione Formato  
Carobbi_2016_Metrologia_53_S74.pdf

non disponibili

Tipologia: Versione editoriale
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 541.06 kB
Formato Adobe PDF
541.06 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Accepted_Carobbi_2016_Metrologia_53_S74.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Documento in Post-print
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 318.2 kB
Formato Adobe PDF
318.2 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11696/54532
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 18
  • ???jsp.display-item.citation.isi??? 15
social impact