[[alternative]]Bayesian Variable Sampling Schemes with and without Accelerated Life Testing for Exponential and Related Models with Type I, II and Random Censoring

計畫編號：NSC88-2213-E032-028研究期間：199808~199907研究經費：283,000[[sponsorship]]行政院國家科學委員

A Multiple Dependent State Repetitive Sampling Plan Based on Performance Index for Lifetime Data with Type II Censoring

In this paper, a multiple dependent state repetitive (MDSR) sampling plan based on the lifetime performance index C-L is proposed for lifetime data with type II censoring when the lifetime of a product follows the exponential distribution or Weibull distribution. The optimal parameters of the proposed plan are determined by minimizing the average sample number while satisfying the producer's risk and consumer's risk at corresponding quality levels. Besides, the performance of the proposed plan is compared with that of the existing lifetime sampling plan in terms of the average sample number and operating characteristic curve. Two illustrative examples are given for the demonstration of the proposed plan.11Ysciescopu

Bayesian sampling plans with interval censoring

This paper employs Bayesian approach to establish acceptance sampling plans for life tests with interval censoring. Assume that interval data have a multinomial distribution, and the interval probabilities are random and vary from lot to lot according to a conjugate prior of Dirichlet distribution. A Bayes risk is defined with a suitable loss function and a predictive distribution. Optimal Bayesian sampling plans are determined by minimizing the Bayes risk per lot. An example is used and some optimal Bayesian sampling plans with three equally-spaced intervals are tabulated for illustration. Sensitivity analysis are conducted to evaluate the influence of the parameter of prior distribution, the cost per sampled item and the cost per used unit time on the proposed Bayesian sampling plans

Bayesian approach to variable sampling plans for the Weibull distribution with censoring.

by Jian-Wei Chen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 84-86).Chapter Chapter 1 --- IntroductionChapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Bayesian approach to single variable sampling plan for the exponential distribution --- p.3Chapter 1.3 --- Outline of the thesis --- p.7Chapter Chapter 2 --- Single Variable Sampling Plan With Type II CensoringChapter 2.1 --- Model --- p.10Chapter 2.2 --- Loss function and finite algorithm --- p.13Chapter 2.3 --- Numerical examples and sensitivity analysis --- p.17Chapter Chapter 3 --- Double Variable Sampling Plan With Type II CensoringChapter 3.1 --- Model --- p.25Chapter 3.2 --- Loss function and Bayes risk --- p.27Chapter 3.3 --- Discretization method and numerical analysis --- p.33Chapter Chapter 4 --- Bayesian Approach to Single Variable Sampling Plans for General Life Distribution with Type I CensoringChapter 4.1 --- Model --- p.42Chapter 4.2 --- The case of the Weibull distribution --- p.47Chapter 4.3 --- The case of the two-parameter exponential distribution --- p.49Chapter 4.4 --- The case of the gamma distribution --- p.52Chapter 4.5 --- Numerical examples and sensitivity analysis --- p.54Chapter Chapter 5 --- DiscussionsChapter 5.1 --- Comparison between Bayesian variable sampling plans and OC curve sampling plans --- p.63Chapter 5.2 --- Comparison between single and double sampling plans --- p.64Chapter 5.3 --- Comparison of both models --- p.66Chapter 5.4 --- Choice of parameters and coefficients --- p.66Appendix --- p.78References --- p.8

Applying the Bayesian Technique in Designing a Single Sampling Plan

 The Bayesian sampling plans for production inspection are considered a technique of sampling inspection techniques for determining the characteristics of the sampling plan based on the assumption that the rate of defectives is a random variable that varies from one production batch to the next, resulting in a probability distribution f(p) that could be determined based on experience and the available quality information available. As part of this study, the parameters of a single Bayesian sampling plan (n,c) were derived by using the Beta-Binomial distribution and compared with those of other single sampling plans. Researchers have identified (ALA company for soft drinks), which handles product quality control. 120 production batches were selected, and the size of the batch and the number of defective items were used to determine the proportion of defective items, given that the variable varies randomly from one production batch to the next. Bayesian and decision-making models can be implemented to create a single sampling inspection process that is close to the actual quality level. The researchers discovered that when the decision-making model was used, the sample size was minimal compared to other inspection plans, leading to a low inspection cost