[1] Asada M. (2001), Wafer yield prediction by the Mahalanobis-Taguchi system; IEEE International
Workshop on Statistical Methodology 6; 25-28.
[2] Fowlkes W.Y., Creveling C.M. (1995), Engineering methods for robust product design; Addison-
Wesley, Reading, MA.
[3] Hong J., Cudney E.A., Taguchi G., Jugulum R., Paryani K., Ragsdell K. (2005), A comparison study of
Mahalanobis-Taguchi system and neural network for multivariate pattern recognition, ASME IMECE,
Orlando, FL.
[4] Jugulum R., Monplaisir L. (2002), Comparison between Mahalanobis-Taguchi system and artificial
neural networks; Journal of Quality Engineering Society 10(1); 60-73.
[5] Kiemele M.J., Schmidt S.R., Berdine R.J. (1999), Basic statistics: Tools for continuous improvement;
Air Academy Press, Colorado Springs, CO.
[6] Lande U., Mahalanobis distance: A theoretical and practical approach,
http://biologi.uio.no/fellesavdelinger/finse/spatialstats/Mahalanobis%20distance.ppt, July 11, 2004.
[7] Manly B.F.J. (1994), Multivariate statistical methods: A primer; Chapman & Hall, London.
[8] Taguchi G., Jugulum R. (2002), The Mahalanobis-Taguchi strategy; John Wiley & Sons, Inc., New
York.
[9] Wu Y. (2004), Pattern recognition using Mahalanobis distance; Journal of Quality Engineering Forum
12 (5); 787-795.
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