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Multivariate Zero-Inflated Poisson Models and Their Applications
Chin-Shang Li, Jye-Chyi Lu, Jinho Park, Kyungmoo Kim, Paul A. Brinkley and John P. Peterson
Vol. 41, No. 1 (Feb., 1999), pp. 29-38
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1270992
Page Count: 10
You can always find the topics here!Topics: Maximum likelihood estimation, Simulations, Confidence interval, P values, Estimation bias, Statistical estimation, Standard deviation, Statistical models, Binomials, Parametric models
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The zero-inflated Poisson (ZIP) distribution has been shown to be useful for modeling outcomes of manufacturing processes producing numerous defect-free products. When there are several types of defects, the multivariate ZIP (MZIP) model can be useful to detect specific process equipment problems and to reduce multiple types of defects simultaneously. This article proposes types of MZIP models and investigates distributional properties of an MZIP model. Finite-sample simulation studies show that, compared to the method of moments, the maximum likelihood method has smaller bias and variance, as well as more accurate coverage probability in estimating model parameters and zero-defect probability. Real-life examples from a major electronic equipment manufacturer illustrate how the proposed procedures are useful in a manufacturing environment for equipment-fault detection and for covariate effect studies.
Technometrics © 1999 American Statistical Association