Double ASN Minimax sampling plans by variables when the standard deviation is unknown

Krumbholz, Wolf; Rohr, Andreas

doi:10.1007/s10182-009-0111-8

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Alternatively, we can determine the above parameters of the variables chain sampling plan to minimize the ASN at AQL, which is analogous to minimizing the average sample number in the variables repetitive group sampling plans and multiple dependent state sampling plan (see Balamurali et al [27] and Balamurali and Jun [26]). Some of the authors have investigated the designing of sampling plans by using some other optimization techniques which are available in the literature (see, for example, Feldmann and Krumbholz [28], Krumbholz and Rohr [29,30], Krumbholz et al [31], and Duarte and Sariava [32,33]. The ASN for the chain sampling plan is the sample size only.…”

Section: Designing Methodology Of Variables Chain Sampling Planmentioning

confidence: 99%

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Balamurali

Usha

2013

International Journal of Manufacturing Engineering

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We investigate the optimal designing of chain sampling plan for the application of normally distributed quality characteristics. The chain sampling plan is one of the conditional sampling procedures and this plan under variables inspection will be useful when testing is costly and destructive. The advantages of this proposed variables plan over variables single sampling plan and variables double sampling plan are discussed. Tables are also constructed for the selection of optimal parameters of known and unknown standard deviation variables chain sampling plan for specified two points on the operating characteristic curve, namely, the acceptable quality level and the limiting quality level, along with the producer’s and consumer’s risks. The optimization problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve.

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Section: Designing Methodology Of Variables Chain Sampling Planmentioning

confidence: 99%

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Balamurali

Usha

2013

International Journal of Manufacturing Engineering

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“…The problem becomes even more complicated when considering the fact that the critical value k 3 of the second stage does not occur in the objective function (the N max -criterion). An algorithm for solving this problem was presented in detail by Krumbholz and Rohr (2009) and Rohr (2009).…”

Section: The Minimax Version Of the Two-stage T Testmentioning

confidence: 99%

“…Remark 2 Krumbholz and Rohr (2009) denote the minimax version of their double sampling plan for variables as AM plan (ASN-Minimax plan). For given AQL (= Acceptable Quality Level) p 1 , LQ (= Limiting Quality) p 2 , and niveaus α, β the AM plan has the minimal ASN maximum among all double plans obeying…”

Section: The Minimax Version Of the Two-stage T Testmentioning

confidence: 99%

Minimax versions of the two-stage t test

2010

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Let X be a N (μ, σ 2 ) distributed characteristic with unknown σ . We present the minimax version of the two-stage t test having minimal maximal average sample size among all two-stage t tests obeying the classical two-point-condition on the operation characteristic. We give several examples. Furthermore, the minimax version of the two-stage t test is compared with the corresponding two-stage Gauß test.Keywords t Test · Two-stage t test · Average sample number · Minimax version of the two-stage t and Gauß test

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“…where φ * 2 = n 1 l 1 l 2 n 2 l 3 denotes the AM-double sampling plan in case of an upper tolerance limit U (cf. Krumbholz and Rohr (2009)). φ * 2 is determined by…”

Section: The P (A 2 )mentioning

confidence: 99%

Essentially ML ASN-Minimax double sampling plans

Vangjeli

2011

Preprint

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Subject of this paper is ASN-Minimax (AM) double sampling plans by variables for a normally distributed quality characteristic with unknown standard deviation and two-sided specification limits. Based on the estimator p * of the fraction defective p, which is essentially the Maximum-Likelihood (ML) estimator, AM-double sampling plans are calculated by using the random variables p * 1 and p * p relating to the first and pooled samples, respectively. Given p 1 , p 2 , α, and β, no other AM-double sampling plans based on the same estimator feature a lower maximum of the average sample number (ASN) while fulfilling the classical two-point condition on the corresponding operation characteristic (OC).

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Double ASN Minimax sampling plans by variables when the standard deviation is unknown

Abstract: Double sampling plans by variables, ASN Minimax plan,

Cited by 6 publications

References 7 publications

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Optimal Designing of Variables Chain Sampling Plan by Minimizing the Average Sample Number

Minimax versions of the two-stage t test

Essentially ML ASN-Minimax double sampling plans

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