Statistische Qualitätskontrolle

Uhlmann, Werner

doi:10.1007/978-3-322-84871-0

Cited by 63 publications

(9 citation statements)

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“…[2]). Unfortunately, this framework does not reduce the shortcomings of sampling inspections by attributes caused by the problem of adequacy.…”

Section: Introductionmentioning

confidence: 93%

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A generalized framework of sampling inspections by attributes

̈tschmer

2005

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This paper tries to establish a framework for sampling inspections by attributes based on soft quality standards to pay more attention to some shortcomings in classical modelling concerning the acceptability of inspection lots. As a suggestion, such sampling inspections are described in terms of a statistical test with fuzzy hypothesis and fuzzy data, extending the classical framework of sampling inspections by attributes. Furthermore, the operating characteristics will be explored, and single sampling plans of given strength are investigated.

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“…[2]). Unfortunately, this framework does not reduce the shortcomings of sampling inspections by attributes caused by the problem of adequacy.…”

Section: Introductionmentioning

confidence: 93%

“…A well-developed framework is available to controllers when dealing with sampling inspections by attributes [1,2]. Within statistical terminology, sampling inspections by attributes can be described as one-sided statistical tests about the portion of the defective units in the inspection lot under consideration.…”

Section: Introductionmentioning

confidence: 99%

A generalized framework of sampling inspections by attributes

̈tschmer

2005

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“…Die Statistics Toolbox von MATLAB stellt hierfür die Funktion nlinfit zur Verfügung, welche nichtlineare Regressionsprobleme mit Hilfe eines Gauß-Newton-Verfahrens zu lösen versucht 34 . Auch im nichtlinearen Fall kann der Lösungsansatz verfolgt werden, die Parameter so zu variieren, dass die Summe der Fehlerquadrate zwischen vorgegebenen und vom Modell geschätzten Werten der Zielgröße minimiert wird.…”

Section: Allgemeine Nichtlineare Regressionsmodelleunclassified

Wahrscheinlichkeitsrechnung und Statistik mit MATLAB

2005

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“…For instance, Z immer and B urr (1963) studied variables based on Burr's distribution. When the distribution is known up to a location parameter, the problem has been studied by B ehl (1981) and U hlmann (1983). In this paper, we propose a nonparametric solution for the general case.…”

Section: Introductionmentioning

confidence: 99%

Sampling inspection by variables: nonparametric setting

Steland

Zähle

2009

Statistica Neerlandica

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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.

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Statistische Qualitätskontrolle

Cited by 63 publications

References 0 publications

A generalized framework of sampling inspections by attributes

A generalized framework of sampling inspections by attributes

Wahrscheinlichkeitsrechnung und Statistik mit MATLAB

Sampling inspection by variables: nonparametric setting

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