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Multivariate process capability using principal component analysis in the presence of measurement errors

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  • Michele Scagliarini, 2011. "Multivariate process capability using principal component analysis in the presence of measurement errors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 113-128, June.
    • Handle: RePEc:spr:alstar:v:95:y:2011:i:2:p:113-128
    DOI: 10.1007/s10182-011-0156-3
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    References listed on IDEAS

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    1. W. L. Pearn & M. H. Shu & B. M. Hsu, 2005. "Testing process capability based on Cpm in the presence of random measurement errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(10), pages 1003-1024.
    2. W. L. Pearn & F. K. Wang & C. H. Yen, 2007. "Multivariate Capability Indices: Distributional and Inferential Properties," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(8), pages 941-962.
    3. Wu, Chien-Wei & Pearn, W.L. & Kotz, Samuel, 2009. "An overview of theory and practice on process capability indices for quality assurance," International Journal of Production Economics, Elsevier, vol. 117(2), pages 338-359, February.
    4. Michele Scagliarini, 2010. "Inference on Cpk for autocorrelated data in the presence of random measurement errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 147-158.
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    Cited by:

    1. Daniela F. Dianda & Marta B. Quaglino & José A. Pagura, 2018. "Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 117-143, January.

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