The MVPMODEL Procedure


  • Alt, F. (1985), “Multivariate Quality Control,” in Encyclopedia of Statistical Sciences, volume 6, New York: John Wiley & Sons.

  • Cooley, W. W. and Lohnes, P. R. (1971), Multivariate Data Analysis, New York: John Wiley & Sons.

  • Eastment, H. T. and Krzanowski, W. J. (1982), “Cross-Validatory Choice of the Number of Components from a Principal Component Analysis,” Technometrics, 24, 73–77.

  • Gnanadesikan, R. (1977), Methods for Statistical Data Analysis of Multivariate Observations, New York: John Wiley & Sons.

  • Hotelling, H. (1933), “Analysis of a Complex of Statistical Variables into Principal Components,” Journal of Educational Psychology, 24, 417–441, 498–520.

  • Jackson, J. E. (1991), A User’s Guide to Principal Components, New York: John Wiley & Sons.

  • Kourti, T. and MacGregor, J. F. (1995), “Process Analysis, Monitoring and Diagnosis, Using Multivariate Projection Methods,” Chemometrics and Intelligent Laboratory Systems, 28, 3–21.

  • Kourti, T. and MacGregor, J. F. (1996), “Multivariate SPC Methods for Process and Product Monitoring,” Journal of Quality Technology, 28, 409–428.

  • Kshirsagar, A. M. (1972), Multivariate Analysis, New York: Marcel Dekker.

  • Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979), Multivariate Analysis, London: Academic Press.

  • McReynolds, W. O. (1970), “Characterization of Some Liquid Phases,” Journal of Chromatographic Science, 8, 685–691.

  • Miller, P., Swanson, R. E., and Heckler, C. H. E. (1998), “Contribution Plots: A Missing Link in Multivariate Quality Control,” Applied Mathematics and Computer Science, 8, 775–792.

  • Morrison, D. F. (1976), Multivariate Statistical Methods, Second Edition, New York: McGraw-Hill.

  • Pearson, K. (1901), “On Lines and Planes of Closest Fit to Systems of Points in Space,” Philosophical Magazine, 6, 559–572.

  • Rao, C. R. (1964), “The Use and Interpretation of Principal Component Analysis in Applied Research,” Sankhy$\bar{a}$, Series A, 26, 329–358.

  • Wold, S. (1978), “Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models,” Technometrics, 20, 397–405.