Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 5 Articles
This paper proposes a distribution-free (or nonparametric) control scheme to monitor a process output that contains two special\r\ncauses of variation called ââ?¬Å?block or batchââ?¬Â effects and ââ?¬Å?treatment or positionââ?¬Â effects. The scheme properties (control limits, false\r\nalarmrate, and in-control average run length) stay the same under any assumed continuous probability distribution. Formoderate\r\nsample sizes, these properties can be computed exactly from available tables without the need to estimate the mean or variance of\r\nthe process. The proposed monitoring scheme requires ranking the observations within blocks and using the method of analysis\r\nof means by ranks. The paper includes an illustrative example concerning the grinding process of silicon wafers used in integrated\r\ncircuits production...
This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad\r\nchange in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the\r\navailability of a reference (or training) sample of observations taken when the process was operating in-control. The charting\r\nstatistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional\r\naverage run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the\r\nShewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all\r\n(symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a\r\none-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness\r\nand/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart\r\nwhen the underlying process distribution has tails heavier than those of the normal distribution....
When dealing with complex systems, all decision making occurs under some level of uncertainty. This is due to the physical\r\nattributes of the system being analyzed, the environment in which the system operates, and the individuals which operate the\r\nsystem. Techniques for decision making that rely on traditional probability theory have been extensively pursued to incorporate\r\nthese inherent aleatory uncertainties. However, complex problems also typically include epistemic uncertainties that result from\r\nlack of knowledge. These problems are fundamentally different and cannot be addressed in the same fashion. In these instances,\r\ndecision makers typically use subject matter expert judgment to assist in the analysis of uncertainty. The difficulty with expert\r\nanalysis, however, is in assessing the accuracy of the expert�s input. The credibility of different information can vary widely\r\ndepending on the expert�s familiarity with the subject matter and their intentional (i.e., a preference for one alternative over\r\nanother) and unintentional biases (heuristics, anchoring, etc.). This paper proposes the metric of evidential credibility to deal with\r\nthis issue. The proposed approach is ultimately demonstrated on an example problem concerned with the estimation of aircraft\r\nmaintenance times for the Turkish Air Force....
This paper includes the Bayesian analysis of Burr type VII distribution. Three censoring schemes, namely, left censoring, singly type\r\nII censoring, and doubly type II censoring have been used for posterior estimation. The results of different censoring schemes have\r\nbeen compared with those under complete samples. The comparative study among the performance of different censoring schemes\r\nhas also been made. Two noninformative (uniform and Jeffreys) priors have been assumed to derive the posterior distributions\r\nunder each case. The performance of Bayes estimators has been compared in terms of posterior risks under a simulation\r\nstudy....
Using numerical simulation, the finite-sample properties of threshold autoregressive TAR and\r\nmomentum-threshold MTAR autoregressive-based unit root tests under both deterministic\r\nand consistent methods of threshold estimation are examined in the presence of generalised\r\nautoregressive conditional heteroskedasticity GARCH. Previous research is extended by\r\nconsidering both the impact of alternative robust methods of covariance matrix estimation and\r\nthe behaviour of the secondary tests of asymmetry associated with the TAR and MTAR models.\r\nThe results obtained reveal many interesting features, in particular the distortionary effects of\r\nconsistent-threshold estimation. In summary, the findings presented indicate that caution should\r\nbe exercised when interpreting the results of these frequently employed threshold-based testing\r\nmethods....
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