Design of truncated repetitive sampling plan for poisson count data using expected sampling risks
- Autores: Carlos J. Pérez González, Arturo Javier Fernández Rodríguez
- Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 398-401
- Idioma: inglés
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Resumen
Single and repetitive sampling plans represent conventional methods used in inspection the quality of lots or batches of products. Truncated repetitive inspection presented in this paper allows the practitioners to signi cantly reduce the required sampling e ort from the lot. In this scheme, the lots can be reinspected, at most, a pre xed number of times when their acceptance or rejection cannot be concluded from the original inspection. We develop the design of truncated repetitive sampling plans based on defect count data and using expected sampling risks. The Poisson distribution is assumed for the number of defects found in the sample and a gamma prior model on the unknown defect rate is considered. The optimal truncated repetitive sampling plans are obtained by solving several nonlinear programming problems. The results show that optimal truncated plans are better than the conventional single and repetitive schemes in reducing the average sample number of the inspection.
