scholarly journals Sampling plans using extended EWMA statistic with and without auxiliary information

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Muhammad Naveed ◽  
◽  
Muhammad Azam ◽  
Muhammad Saeed ◽  
Nasrullah Khan ◽  
...  
Keyword(s):  
Auxiliary Information ◽  
Bivariate Normal Distribution ◽  
Quality Trait ◽  
Acceptance Sampling ◽  
Study Variable ◽  
Sampling Plans ◽  
Bivariate Normal ◽  
Acceptance Sampling Plan ◽  
Population Standard Deviation ◽  
Industrial Use

In this paper, we propose an Acceptance Sampling Plan (ASP) using the statistic suggested by Naveed et al. (2018) under the condition of known and unknown population standard deviation (SD) for the presence of with and without Auxiliary Information (AI). It is presumed that the study variable of quality trait and AI follow the bivariate normal distribution. The plan parameters of the recommended plan are discussed for all four cases under the constraint that specified producer and consumer risks are gratified. The suggested plan is compared in terms of sample size (SS) with numerous existing plans and showed that the presented plan has a smaller SS for any value of AQL, LQL. Various tables of plan parameters have been erected using various combinations of smoothing constants for industrial use. For the workable purpose, the industrial example has also been examined. At last, concluding remarks are discussed.

scholarly journals Design of Sampling Plan Using Regression Estimator under Indeterminacy

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 754 ◽  
Author(s):  
Muhammad Aslam ◽  
Ali AL-Marshadi
Keyword(s):  
Auxiliary Information ◽  
Sampling Plan ◽  
Acceptance Sampling ◽  
Regression Estimator ◽  
Sampling Plans ◽  
Classical Statistics ◽  
Acceptance Sampling Plans ◽  
Neutrosophic Statistics ◽  
Lot Sentencing

The acceptance sampling plans are one of the most important tools for the inspection of a lot of products. Sometimes, it is difficult to study the variable of interest, and some additional or auxiliary information which is correlated to that variable is available. The existing sampling plans having auxiliary information are applied when the full, precise, determinate and clear data is available for lot sentencing. Neutrosophic statistics, which is the extension of classical statistics, can be applied when information about the quality of interest or auxiliary information is unclear and indeterminate. In this paper, we will introduce a neutrosophic regression estimator. We will design a new sampling plan using the neutrosophic regression estimator. The neutrosophic parameters of the proposed plan will be determined through the neutrosophic optimization solution. The efficiency of the proposed plan is discussed. The results of the proposed plan will be explained using real industrial data. From the comparison, it is concluded that the proposed sampling plan is more effective and adequate for the inspection of a lot than the existing plan, under the conditions of uncertainty.

scholarly journals Economic Design of Acceptance Sampling Plans for Truncated Life Tests Using Three-Parameter Lindley Distribution

2020 ◽  
Vol 18 (2) ◽  
pp. 2-15 ◽  
Author(s):  
Amer Ibrahim Al-Omari ◽  
Enrico Ciavolino ◽  
Amjad D. Al-Nasser
Keyword(s):  
Operating Characteristic ◽  
Average Lifetime ◽  
Acceptance Sampling ◽  
Sampling Plans ◽  
Lindley Distribution ◽  
Confidence Levels ◽  
Acceptance Sampling Plan ◽  
Acceptance Sampling Plans ◽  
Truncated Life Test ◽  
Life Tests

A single acceptance sampling plan for the three-parameter Lindley distribution under a truncated life test is developed. For various consumer’s confidence levels, acceptance numbers, and values of the ratio of the experimental time to the specified average lifetime, the minimum sample size important to assert a certain average lifetime are calculated. The operating characteristic (OC) function values as well as the associated producer’s risks are also provided. A numerical example is presented to illustrate the suggested acceptance sampling plans.

scholarly journals Design of Fuzzy Sampling Plan Using the Birnbaum-Saunders Distribution

Mathematics ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 9 ◽  
Author(s):  
Muhammad Zahir Khan ◽  
Muhammad Farid Khan ◽  
Muhammad Aslam ◽  
Abdur Razzaque Mughal
Keyword(s):  
Operating Characteristic ◽  
Real Life ◽  
Sampling Plan ◽  
Acceptance Sampling ◽  
Acceptance Probability ◽  
Defective Items ◽  
Sampling Plans ◽  
Fuzzy Environment ◽  
Acceptance Sampling Plan ◽  
Acceptance Sampling Plans

Acceptance sampling is one of the essential areas of quality control. In a conventional environment, probability theory is used to study acceptance sampling plans. In some situations, it is not possible to apply conventional techniques due to vagueness in the values emerging from the complexities of processor measurement methods. There are two types of acceptance sampling plans: attribute and variable. One of the important elements in attribute acceptance sampling is the proportion of defective items. In some situations, this proportion is not a precise value, but vague. In this case, it is suitable to apply flexible techniques to study the fuzzy proportion. Fuzzy set theory is used to investigate such concepts. It is observed there is no research available to apply Birnbaum-Saunders distribution in fuzzy acceptance sampling. In this article, it is assumed that the proportion of defective items is fuzzy and follows the Birnbaum-Saunders distribution. A single acceptance sampling plan, based on binomial distribution, is used to design the fuzzy operating characteristic (FOC) curve. Results are illustrated with examples. One real-life example is also presented in the article. The results show the behavior of curves with different combinations of parameters of Birnbaum-Saunders distribution. The novelty of this study is to use the probability distribution function of Birnbaum-Saunders distribution as a proportion of defective items and find the acceptance probability in a fuzzy environment. This is an application of Birnbaum-Saunders distribution in fuzzy acceptance sampling.

A note on the generalization of bivariate normal quadrant probabilities to bivariate elliptical distributions

2018 ◽  
Author(s):  
Oscar Lorenzo Olvera Astivia
Keyword(s):  
Normal Distribution ◽  
Bivariate Normal Distribution ◽  
Elliptical Distributions ◽  
Bivariate Normal ◽  
Geometric Argument

I present a geometric argument to show that the quadrant probability for the bivariate normal distribution can be generalized to the case of all elliptical distributions.

Group acceptance sampling plan for resubmitted lots based on life tests for odds exponential log logistic distribution

2017 ◽  
Vol 34 (8) ◽  
pp. 1343-1351 ◽  
Author(s):  
Rosaiah K. ◽  
Srinivasa Rao Gadde ◽  
Kalyani K. ◽  
Sivakumar D.C.U.
Keyword(s):  
Group Size ◽  
Sampling Plan ◽  
Logistic Distribution ◽  
Acceptance Sampling ◽  
Consumer’S Risk ◽  
Content Type ◽  
Acceptance Sampling Plan ◽  
Minimum Group ◽  
Termination Time ◽  
Group Acceptance

Purpose The purpose of this paper is to develop a group acceptance sampling plan (GASP) for a resubmitted lot when the lifetime of a product follows odds exponential log logistic distribution introduced by Rao and Rao (2014). The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time. The authors compare the proposed plan with the ordinary GASP, and the results are illustrated with live data example. Design/methodology/approach The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time. Findings The authors determined the group size and acceptance number. Research limitations/implications No specific limitations. Practical implications This methodology can be applicable in industry to study quality control. Social implications This methodology can be applicable in health study. Originality/value The parameters of the proposed plan such as minimum group size and acceptance number are determined for a pre-specified consumer’s risk, number of testers and the test termination time.

scholarly journals PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 230-231
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecan ◽  
Kadir Kizilkaya ◽  
Milt Thomas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Network Models ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Neural Network Models ◽  
Bivariate Normal ◽  
Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

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