Multivariate Data Analysis
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Listed below are some excellent sources for learning more about multivariate data analysis methods.
Please feel free to make suggestions of items to add to our list, via e-mail to info@umetrics.com. We appreciate your input.

Papers

PLS applications

  • Trygg J, Wold S - "O2-PLS, a two-block (X-Y) latent variable regression (LVR) method with an integral OSC filter", Journal of Chemometrics, 17: 53-64, 2003.
  • Trygg J, Wold S - "Orthogonal projections to latent structures (O-PLS)", Journal of Chemometrics, 16: 119-128, 2002.
  • Trygg J - "O2-PLS for qualitative and quantitative analysis in multivariate calibration", Journal of Chemometrics, 16: 283-293, 2002.
  • Wold S, Trygg J, Berglund A, Antti H - "Some recent developments in PLS modeling", Chemometrics and Intelligent Laboratory Systems, 58: 131-150, 2001.
  • Stone M, Brooks R J – “Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal components regression” Journal of the Royal Statistical Society, Ser. B, 52, 237–269, 1990.
  • Geladi P – “Notes on the history and nature of partial least sqares (PLS) modeling”, Journal of Chemometrics, Vol.2, p.231–246, 1988.
  • Höskuldsson A – “PLS regression methods” Journal of Chemometrics, 2, 211–228, 1988.
  • Wold S, Albano C, Dunn WJ, et al. – “Modeling data tables by principal components and PLS: Class patterns and quantitative predictive relations” Analusis 12, 477–485, 1984.
On-line applications
  • Yoon S, et al. – “Multivariate Process Monitoring and Early Fault Detection (MSPC) using PCA and PLS.” NPRA Plant Automation and Decision Support Conference, 2003. MSPC Applications at Honeywell (NPRA) (112 kB pdf)
  • MacGregor J F – “Using, on-line process data to improve quality” ASQC Statistics division newsletter, vol. 16. NO. 2. Page 6–13, 1996.
  • Kourti T, MacGregor J F –“Process analysis, monitoring and diagnosis, using multivariate projection methods” Chemometrics and Intelligent Laboratory Systems, 28,1995, 3–21, 1995.
  • Kresta J V, MacGregor J F, Marlin T E – “Multivariate statistical monitoring of process operating performance” The Canadian Journal of Chemical Engineering, 69, 35–47, 1991.
PCA applications
  • Ingenbleek, J-F – “What is a sportscar?” Astin Bulletin, 18, No. 2, 175–187, 1988.
  • Wold S, Esbensen K, Geladi P – “Principal component analysis.” Chemometrics and Intelligent Laboratory Systems 2, 37–52, 1987.
General multivariate data analysis
  • Wold S – “Exponentially weighted moving principal components analysis and projections to latent structures” Chemometrics and intelligent laboratory systems, 23, 149–161, 1994.
  • Frank I E, Friedman J H – “A statistical view of some chemometrics regression tools” Technometrics, 35, 109–148, 1993.
  • Wold S, Albano C, Dunn W J, et al. – “Multivariate Data Analysis: Converting Chemical Data tables to plots.” In: “Computer applications in chemical research and education.” Heidelberg, Dr. Alfred Hütig Verlag, 1989.
Multivariate data analysis and design of experiments
  • Eriksson L, Johansson E – “Multivariate design and modeling in QSAR” Chemometrics and Intelligent Laboratory Systems, 741, 1996.
  • Ståhle L, Wold S – “Multivariate data analysis and experimental design in biomedical research” In: Ellis GP, West GB (Eds) Progress in Medical Chemistry. Elsevier Science Publishers, 291–338, 1988.
Books

Introduction to multivariate data analysis
  • L. Eriksson, E. Johansson, N. Kettaneh-Wold, J.Trygg, C. Wikström, and S. Wold - Multi- and Megavariate Data Analysis Part I: Basic Principles and Applications, Second revised and enlarged edition, 2006.
    Order your copy here.
  • L. Eriksson, E. Johansson, N. Kettaneh-Wold, J.Trygg, C. Wikström, and S. Wold - Multi- and Megavariate Data Analysis Part II: Advanced Applications and Method Extensions, Second reviced and enlarged edition, 2006.
    Order your copy here.
  • Chatfield C, Collins AJ – Introduction to multivariate analysis. London New York: Chapman and Hall, (ISBN 0-412-16040-4), 1980.
General introduction to uni- and bivariate data analysis
  • Colton T – Statistics in medicine. Boston: Little, Brown and Company, (ISBN 0-315-15250-1), 1974.
  • Chatfield C – Statistics for technology. London New York, Chapman and Hall (ISBN 0-412-25340-2), 1983.
  • McNeese W H , Klein R A – Statistical methods for the process industries, Wisconsin, ASQC Quality Press. (ISBN 0-8247-8524-X), 1991.
Miscallenous
  • Antologi – Anvendelse av Kjemometri innen forskning og industri, Tidsskriftbolaget Kjemi AS. (ISBN 82-91294-01-1), 1996.
  • Höskuldsson Agnar – Prediction Methods in Science and Technology. Thor Publishing Denmark. (ISBN 87-985941-0-9), 1996.
  • Jackson JE – A users guide to principal components. New York: John Wiley (ISBN 0-471-62267-2), 1991.
  • Jollife IT – Principal Component Analysis. New York Berlin Heidelberg Tokyo: Springer-Verlag (ISBN 0-387-95442-2), 2002.
  • Martens H, Naes T – Multivariate calibration, New York: John Wiley, 1989.