Formulae for Calculating the Operating Characteristic and the Average Sample Number of Some Sequential Tests

1958 ◽  
Vol 20 (2) ◽  
pp. 379-386 ◽  
Author(s):  
K. W. Kemp
Keyword(s):  
Operating Characteristic ◽  
Sample Number ◽  
Average Sample Number ◽  
Sequential Tests ◽  
Average Sample

scholarly journals Mixed Variables-Attributes Test Plans for Single and Double Acceptance Sampling under Exponential Distribution

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Yan Li ◽  
Xiaolong Pu ◽  
Dongdong Xiang
Keyword(s):  
Exponential Distribution ◽  
Operating Characteristic ◽  
Acceptance Sampling ◽  
Sample Sizes ◽  
Sample Number ◽  
Sampling Plans ◽  
Average Sample Number ◽  
Mixed Variables ◽  
Average Sample

The mixed variables-attributes test plans for single acceptance sampling are proposed to protect “good lots” from attributes aspect and to optimize sample sizes from variables aspect. For the single and double mixed plans, exact formulas of the operating characteristic and average sample number are developed for the exponential distribution. Numerical illustrations show that the mixed sampling plans have some advantages over the variables plans or attributes plans alone.

scholarly journals Control of Traffic Intensity in Hyperexponential and Mixed Erlang Queueing Systems with a Method Based on SPRT

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Müjgan Zobu ◽  
Vedat Sağlam
Keyword(s):  
Operating Characteristic ◽  
Queueing Systems ◽  
Ratio Test ◽  
Traffic Intensity ◽  
Sample Number ◽  
Average Sample Number ◽  
Phase Type ◽  
Sequential Probability ◽  
Matlab Programming ◽  
Average Sample

The control of traffic intensity is one of the important problems in the study of queueing systems. Rao et al. (1984) developed a method to detect changes in the traffic intensity in queueing systems of the and types based on the Sequential Probability Ratio Test (SPRT). In this paper, SPRT is theoretically investigated for two different phase-type queueing systems which consist of hyperexponential and mixed Erlang. Also, for testing against , Operating Characteristic (OC) and Average Sample Number (ASN) functions are obtained with numerical methods using multipoint derivative equations according to different situations of and type errors. Afterward, numerical illustrations for each model are provided with Matlab programming.

The impact of misspecification of the negative binomial shape parameter in sequential sampling plans

1986 ◽  
Vol 16 (3) ◽  
pp. 608-611 ◽  
Author(s):  
W. G. Warren ◽  
Pin Whei Chen
Keyword(s):  
Operating Characteristic ◽  
Shape Parameter ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Forest Pests ◽  
Sample Number ◽  
Sampling Plans ◽  
Average Sample Number ◽  
The Impact ◽  
Average Sample

Standard sequential sampling plans for determining whether infestations of forest pests have attained critical levels are commonly based on the assumption that the counts follow a negative binomial distribution for which the shape parameter, k, which must be specified, may be difficult to estimate and may well be unstable. This paper studies the effect of misspecification of this parameter on the operating characteristic and average sample number functions of a sequential sampling plan. It appears that slight underestimation of the shape parameter can improve the operating characteristic at little cost, i.e., with only small increase in the average sample number.

scholarly journals Design of SkSP-R Variables Sampling Plans

2015 ◽  
Vol 38 (2) ◽  
pp. 413-429 ◽  
Author(s):  
Muhammad Aslam ◽  
Saminathan Balamurali ◽  
Chi-Hyuck Jun ◽  
Batool Hussain
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Sampling Plan ◽  
Producer’S Risk ◽  
Sample Number ◽  
Sampling Plans ◽  
Average Sample Number ◽  
Nonlinear Optimization Problem ◽  
Better Than ◽  
Average Sample

In this paper, we present the designing of the skip-lot sampling plan including the re-inspection  called SkSP-R. The plan parameters of the proposed plan are determined through a  nonlinear optimization problem by minimizing the average sample number satisfying both the producer's risk and the consumer's risks. The proposed plan is shown to perform better than the existing sampling plans in terms of the average sample number. The application of the proposed plan is explained with the help of illustrative examples.

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