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Maximum likelihood estimators (MLE) were proposed as alternatives to best guess and least squares methods in computer-generated probability plots. Results show that robust regression algorithms are more accurate than least squares in model statistics. However, least squares performed better than the Hampel and Huber functions in data sets where residuals have an assigned weight of 1.0 and when residuals were less than the threshold criteria.

Computer-Integrated Manufacturing (CIM) and related techniques provide more data faster and more often than was possible in the past. Also, high-quality manufacturing requires methods capable of detecting process changes even when the process is producing in the defects per million range. Past research has shown that the best method for analyzing binary data for most situations was the CUSUM procedure. Today's manufacturing environment requires even faster, more accurate detection of process changes than the CUSUM offers. This paper describes a study in developing a system based on using multiple statistical tests together to attain the extra speed and accuracy needed. The results of the research show that the system performs very well when measured against the baseline method: a CUSUM procedure with an [h.sub.t] value of 4.2. The system outperformed the baseline handily in speed of detection. At some points, the system would have correctly classified the equivalent of over 10% of the total received signals before the baseline. For processes measured in defects per million, the actual time differential would be even more pronounced. In addition, the system performed at least as well in accuracy, providing a composite performance over the test scenarios of 95.02% compared with 94.57% for the baseline., 1. Introduction Attribute data is commonly found in industry today. This data class consists of go/no-go, pass/fail and defective/non-defective decisions. Attribute data, or binary data as it is often referred [...]

Trade-off analysis after optimization of an economic CUSUM control chart is proposed. This procedure exploits the relatively flat response surface in the neighborhood of the optimal design. Comments are made [...]

Decision-theoretic models use Bayes stopping rules to minimize reliability testing time with respect to exponential distribution. These rules take into account the exponential scale-parameter, theta, when analyzing sequential and batch testing cases. The mean probability of theta and the probability of the posterior estimate of theta affect reliability testing time.

Many companies have set Parts per Million (PPM) or Parts per Million Opportunities (PPMO) goals in their quest for continuous improvement. The time-between-events (TBE) CUSUM has been suggested for monitoring the number of good units or the number of opportunities that occurred between discoveries of consecutive bad units. We focus on the robustness of the TBE CUSUM. Robustness, in this case, refers to sensitivity of the procedure to make the proper decisions regarding a shift in the mean defect rate when, in fact, the time between events is not exponential. We examine and report average run lengths (ARLs) under both a Weibull and a lognormal time between events distribution. Our results indicate that the TBE CUSUM is extremely robust for a wide variety of parameter values for both the Weibull and lognormal distributions. The implications of these results in practice imply that users of the TBE CUSUM procedure need not be concerned about departures from the exponential TBE distribution. Practical implementation of the TBE CUSUM procedure is also discussed. [ABSTRACT FROM AUTHOR]

Bayesian burn-in procedures are developed for limited failure populations in which defective items fail soon after they are put into operation and non-defective ones never fail during the technological life of the items. Sequential schemes guaranteeing the prescribed quality of a product are derived based on prior information on the quality of a product and accumulated failure information up to the decision point. Numerical examples are provided. [ABSTRACT FROM AUTHOR]

David Drain Chapman and Hall, 1997, 456 pp. From the Preface we learn that this book' ...was written to provide working engineers and engineering students with the statistical tools they [...]

Operating characteristic (OC) curves are often useful in determining how large a sample is required to detect a specified difference for a particular consumer and producer risk. In this paper, OC curves with Bayes stopping rules for the exponential distribution are developed. Example curves are provided for the sequential and batch testing situations. The power of the test is greater under batch testing. A table illustrates the performance of the plans for stopping at each opportunity in a sample of size 20. Some examples are used to demonstrate the application of the proposed methodology. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A cycle time-throughput curve quantifies the relationship of average cycle time to throughput rates in a manufacturing system. Moreover, it indicates the asymptotic capacity of a system. Such a curve is used to characterize system performance over a range of start rates. Simulation is a fundamental method for generating such curves since simulation can handle the complexity of real systems with acceptable precision and accuracy. A simulation-based cycle time-throughput curve requires a large amount of simulation output data; the precision and accuracy of a simulated curve may be poor if there is insufficient simulation data. To overcome these problems, sequential simulation experiments based on a nonlinear D-optimal design are suggested. Using the nonlinear shape of the curve, such a design pinpoints p starting design points, and then sequentially ranks the remaining n=p candidate design points, where n is the total number of possible design points being considered. A model of a semiconductor wafer fabrication facility is used to validate the approach. The sequences of experimental runs generated can be used as references for simulation experimenters. [ABSTRACT FROM AUTHOR]