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Sampling inspection by variables: nonparametric setting
Author(s) -
Steland Ansgar,
Zähle Henryk
Publication year - 2009
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2008.00413.x
Subject(s) - asymptotically optimal algorithm , sampling (signal processing) , nonparametric statistics , mathematics , sample size determination , sample (material) , sampling distribution , statistics , calibration , mathematical optimization , computer science , chemistry , filter (signal processing) , chromatography , computer vision
A classic statistical problem is the optimal construction of sampling plans to accept or reject a lot based on a small sample. We propose a new asymptotically optimal solution for acceptance sampling by variables setting where we allow for an arbitrary unknown underlying distribution. In the course of this, we assume that additional sampling information is available, which is often the case in real applications. That information is given by additional measurements which may be affected by a calibration error. Our results show that, first, the proposed decision rule is asymptotically valid under fairly general assumptions. Secondly, the estimated optimal sample size is asymptotically normal. Furthermore, we illustrate our method by a real data analysis and investigate to some extent its finite‐sample properties and the sharpness of our assumptions by simulations.