All Stories

  1. The survey of autobiographical memory (SAM): A novel measure of trait mnemonics in everyday life

    Article • Cortex, June 2013, Elsevier

    Lynne Williams

  2. Multiple factor analysis: principal component analysis for multitable and multiblock data sets

    Article • Wiley Interdisciplinary Reviews Computational Statistics, February 2013, Wiley

    Lynne Williams

  3. Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

    Article • August 2012, Springer Science + Business Media

    Lynne Williams

  4. Effect of Age on Variability in the Production of Text-Based Global Inferences

    Article • PLoS ONE, May 2012, PLOS

    Lynne Williams

  5. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling

    Article • Wiley Interdisciplinary Reviews Computational Statistics, January 2012, Wiley

    Lynne Williams

  6. Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization

    Article • Computational and Mathematical Methods in Medicine, January 2012, Hindawi Publishing Corporation

    Lynne Williams

  7. Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review

    Article • NeuroImage, May 2011, Elsevier

    Lynne Williams

  8. A Tutorial on Multiblock Discriminant Correspondence Analysis (MUDICA): A New Method for Analyzing Discourse Data From Clinical Populations

    Article • Journal of Speech Language and Hearing Research, October 2010, American Speech-Language-Hearing Association (ASHA)

    Lynne Williams

  9. Principal component analysis

    Article • Wiley Interdisciplinary Reviews Computational Statistics, June 2010, Wiley

    Lynne Williams

  10. How to compute reliability estimates and display confidence and tolerance intervals for pattern classifiers using the Bootstrap and 3-way multidimensional scaling (DISTATIS)

    Article • NeuroImage, March 2009, Elsevier

    Lynne Williams