考虑检验错误的集团抽样检验接收概率函数

本文给出存在集团检验误差时的集团抽样检验接受概率计算公式,该公式可用于设计抽样方案、分析集团检验错误对抽样方案的影响.采用计算机模拟抽样检验的方法对给出的公式加以验证,证明了该所给公式的正确性.同时通过对集团检验错误存在与否的抽样检验特征(OC)曲线的比较,指出:以往的集团抽样检验方案设计,都是在假设无检验误差前提下设计的,在抽样检验中,若存在检验错误,会严重影响抽样检验的接收概率,使抽样检验结果不能客观地反映检验批的质量.     

一般寿命分布和定时截尾的Bayes变量抽样方案

林（1994）研究了指数分布和定时截尾的变量抽样方案．本文将讨论一般寿命分布和定时截尾的一次抽样方案．在多项式损失函数的假设下,我们讨论了Weibull分布、双参数指数分布和-分布三种情形,并着重讨论Weibull分布的情形．本文还提出了一个可用于近似地确定最优抽样方案的有报算法,并且进行了灵敏度分析,还同林较早的模型（1990,1994）做了比较．     

混合截尾和定时截尾试验weibull分布抽样方案的比较

混合截尾试验是定时和定数截尾的一种有用的推广。本文研究了Ｗｅｉｂｕｌｌ分布和混合截尾试验的一次抽样方案,并对可靠决策损失函数给出了贝叶斯风险的显式表达式。比较陈和林的模型（１９９９）,我们得混合截尾试验的抽样方案于定时抽样方案。     

严格πps抽样方案在不放回不等概率抽样方案中的可容许性

该文在Ｒ．Ｒ．Ｓａｘｅｎａ等提出的一种易于实施的不等概率不放回抽样方案中讨论严格πps抽样方案的可容许性问题．除了一种情形我们是在基于模型 设计下讨论的外，其余情形在基于纯设计下圆满地解决了这一问题     

记数集团抽样检查接收概率的近似算法及其应用

本文给出记数集团抽样检查接收概率的近似公式及算法, 并且通过与传统近似算法的比较和计算机模拟抽样检查验证, 讨论了该方法的准确性和实用性, 结果表明: 本文提出的接收概率算法明显优于传统算法, 可用于计数型集团检查抽样方案设计而不必考虑一个集团中有2个或更多不合格品混入的问题\bd 该算法已应用于家蚕微粒子病母蛾集团检查2次和多次抽样方案的设计.     

计数集团检查的抽查特征函数

本文给出计数型集团检查一次、二次抽样方案的抽查特征函数及其算法, 并且通过与近似算法的比较和计算机抽样检查模拟, 验证了该方法的正确性, 并对该抽查特征函数性质和应用作了初步讨论.     

以AQL和AOQL及以LQL和AOQL为索引的计量一次抽样方案

对拒收批可100%挑选的情况下,使用方用AOQL(平均检出质量上限)作为验收指标制定抽样方案是顺理成章的.但从生产方来说,更喜欢用AQL(可接收质量水平)制定抽样方案,因为如果生产方的交验质量处在AQL,他的产品将以高概率接收.这样,可从AQL和AOQL两个指标设计抽样方案,同时满足生产方与使用方的要求.这种类型的抽样方案,经抽检最后被接收的各批,其不合格品率不是P(交验批的不合格品率)就是0,是不均匀的,但其平均质量最坏也不会超过规定的AOQL.如果要求每批质量不超过某个不合格品率(极限质量水平,LQL),同时要求被最后接收的各批平均质…     

关于二次抽样方案的ASN的极值

二次抽样方案的平均抽样个数(简记以ASN)是选取二次抽样方案的一个极为重要的依据。ASN曲线在它的定义范围内一般为一条单峰的铃形曲线。在抽样检验的实践中,通常仅注意到整个ASN曲线的形式,而对它的峰值所在的位置及ASN的极值却很少注意。实际上,一般讲,人们总希望ASN的极值越小越好。特别是当我们对产品质量的分布情况,比如对不合格品率的分布缺乏先验的信息时是如此。显然,二次抽样方案的ASN曲线峰值所在的位置也是越远离产品质量的经验分布的众值越好。     

《方差估计引论》

书评:近年来,抽样调查在我国得到愈来愈广泛的应用,它的重要性和作用也日益被各方面所接受和认识。众所周知,抽样调查的主要目的是对总体目标量进行估计,而估计量的方差则是衡量抽样调查的精度,特别是有关抽样误差的一个重要标准。因此对估计量的方差进行控制与估计是抽样设计与分析中的一个十分重要的技术问题。一般的抽样调查教科书在介绍各种抽样方法时,虽然也涉及针对这种抽样方法的目标量估计的方差估计,但是由于实际中真正使用的抽样方案绝少只是一种抽样方法的单独使用,往往是多种方法的结合。根据这种实际抽样所得的复杂样本的方差…     

研究变量抽样方案的决策论方法

本文应用Bayes决策论来研究具有平方损失函数的变量抽样方案。文中建立了一个模型,并导出了Bayes风险的显示表示。通过对最优样本大小上界的估计,我们提出了一个决定最优一次抽样方案的有限算法。     

Compressed limit sampling inspection plans for food safety

The design of attribute sampling inspection plans based on compressed or narrow limits for food safety applications is covered. Artificially compressed limits allow a significant reduction in the number of analytical tests to be carried out while maintaining the risks at predefined levels. The design of optimal sampling plans is discussed for two given points on the operating characteristic curve and especially for the zero acceptance number case. Compressed limit plans matching the attribute plans of the International Commission on Microbiological Specifications for Foods are also given. The case of unknown batch standard deviation is also discussed. Three‐class attribute plans with optimal positions for given microbiological limit M and good manufacturing practices limit m are derived. The proposed plans are illustrated through examples. R software codes to obtain sampling plans are also given. Copyright © 2016 John Wiley & Sons, Ltd.     

Sampling subproblems of heterogeneous Max‐Cut problems and approximation algorithms

Recent work in the analysis of randomized approximation algorithms for NP‐hard optimization problems has involved approximating the solution to a problem by the solution of a related subproblem of constant size, where the subproblem is constructed by sampling elements of the original problem uniformly at random. In light of interest in problems with a heterogeneous structure, for which uniform sampling might be expected to yield suboptimal results, we investigate the use of nonuniform sampling probabilities. We develop and analyze an algorithm which uses a novel sampling method to obtain improved bounds for approximating the Max‐Cut of a graph. In particular, we show that by judicious choice of sampling probabilities one can obtain error bounds that are superior to the ones obtained by uniform sampling, both for unweighted and weighted versions of Max‐Cut. Of at least as much interest as the results we derive are the techniques we use. The first technique is a method to compute a compressed approximate decomposition of a matrix as the product of three smaller matrices, each of which has several appealing properties. The second technique is a method to approximate the feasibility or infeasibility of a large linear program by checking the feasibility or infeasibility of a nonuniformly randomly chosen subprogram of the original linear program. We expect that these and related techniques will prove fruitful for the future development of randomized approximation algorithms for problems whose input instances contain heterogeneities. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2008     

Cyclic BSEC of block size 3

Balanced sampling plans excluding contiguous units (or BSEC) were first introduced by Hedayat, Rao and Stufken in 1988. In this note, we discuss constructions of these designs having cyclic automorphisms. We use Langford sequences to construct all possible cyclic BSEC (or CBSEC) with block size 3 and λ = 1,2, which establishes the necessary and sufficient conditions for such designs. Some constructions of the balanced sampling plan avoiding adjacent units, a generalization of BSEC, are also given for fixed λ.     

Estimation and Design of Sampling Plans for Monitoring Dependent Production Processes

We consider the problem of designing single and the double sampling plans for monitoring dependent production processes. Based on simulated samples from the process, Nelson proposed a new approach of estimating the characteristics of single sampling plans and, using these estimates, designing optimal plans. In this paper, we extend his approach to the design of optimal double sampling plans. We first propose a simple methodology for obtaining the unbiased estimators of various characteristics of single and double sampling plans. This is achieved by defining the various characteristics of sampling plans as explicit random variables. Some of the important properties of the double sampling plans are established. Using these results, an efficient algorithm is developed to obtain optimal double sampling plans. A comparison with a crude search shows that our algorithm leads to about 90% savings, on the average, in computational timings. The procedure is also explained through a suitable example for the ARMA(1,1) model. It is observed, for instance, that an optimal double sampling plan leads to about 23% reduction in average sample number, compared to an optimal single sampling plan. Tables for choosing the optimal plans for certain auto regressive moving average processes at some practically useful values of acceptable quality level and rejectable quality level are also presented.     

Generalized sampling reconstruction from Fourier measurements using compactly supported shearlets

In this paper we study the general reconstruction of a compactly supported function from its Fourier coefficients using compactly supported shearlet systems. We assume that only finitely many Fourier samples of the function are accessible and based on this finite collection of measurements an approximation is sought in a finite dimensional shearlet reconstruction space. We analyze this sampling and reconstruction process by a recently introduced method called generalized sampling. In particular by studying the stable sampling rate of generalized sampling we then show stable recovery of the signal is possible using an almost linear rate. Furthermore, we compare the result to the previously obtained rates for wavelets.     

Statistics and game theory: Estimating coalitional values in R software

In this paper we illustrate the usage of R software in cooperative game theory. In particular, we address the problems that arise in exactly calculating the coalitional values for general TU-games in which a large amount of players is involved. In these settings, their approximation may be useful in practice. We analyse these sampling methods from a statistical point of view, from a theoretical and an empirical approach.     

A new system of skip‐lot sampling plans having a provision for reducing normal inspection

The skip‐lot sampling program can be used for reducing the amount of inspection on a product that has excellent quality history. Thus skip‐lot sampling plans are designed to reduce inspection costs. Moreover, the skip‐lot concept is sound and useful and is economically advantageous to use in the design of sampling plans. Hence, a new system of skip‐lot sampling plans designated as the SkSP‐V plan is developed in this paper. The proposed plan requires a return to normal inspection whenever a lot is rejected during sampling inspection, but has a provision for a reduced normal inspection upon demonstration of superior product quality. A Markov chain formulation and derivation of performance measures for this new plan are presented. The properties of SkSP‐V plan are studied with single sampling plan as the reference plan. Advantages of this new plan are also discussed. Finally, certain cost models are given for the economic design of the SkSP‐V plan. Copyright © 2010 John Wiley & Sons, Ltd.     

Bayesian single and double variable sampling plans for the Weibull distribution with censoring

The sampling inspection problem is one of the main research topics in quality control. In this paper, we employ Bayesian decision theory to study single and double variable sampling plans, for the Weibull distribution, with Type II censoring. A general loss function which includes the sampling cost, the time-consuming cost, the salvage value, and the after-sales cost is proposed to determine the Bayes risk and the corresponding optimal sampling plan. Explicit expressions for the Bayes risks for both single and double sampling plans are derived, respectively. Numerical examples are given to illustrate the effectiveness of the proposed method. Comparisons between single and double sampling plans are made, and sensitivity analysis is performed.