Currently browsing: Items authored or edited by John Gower

Albers, C. J.; Gower, J. C. and Kiers, H. A. L. (2018). Rank Properties for Centred Three-Way Arrays. In: Mola, F.; Conversano, C. and Vichi, M. eds. Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, pp. 69–76.

Albers, Casper and Gower, John (2017). Visualising interactions in bi- and triadditive models for three-way tables. Chemometrics and Intelligent Laboratory Systems, 167 pp. 238–247.

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Albers, C. J.; Critchley, F. and Gower, J. C. (2011). Applications of quadratic minimisation problems in statistics. Journal of Multivariate Analysis, 102(3) pp. 714–722.

Albers, C.J.; Critchley, F. and Gower, J. C. (2011). Quadratic minimisation problems in statistics. Journal of Multivariate Analysis, 102(3) pp. 698–713.

Albers, Casper J. and Gower, John C. (2010). A general approach to handling missing values in Procrustes analysis. Advances in Data Analysis and Classification, 4(4) pp. 223–237.

Arnold, Gillian M; Gower, John C; Gardner-Lubbe, Sugnet and Le Roux, Niël J (2007). Biplots of free-choice profile data in generalized orthogonal Procrustes analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics), 56(4) pp. 445–458.

Albers, Casper; Critchley, Frank and Gower, John (2007). Group average representations in Euclidean distance cones. In: Brito, Paula; Bertrand, Patrice; Cucumel, Gucumel and de Carvalho, Francisco eds. Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Berlin, Germany: Springer, pp. 445–454.

Blasius, Jörg; Eilers, Paul H. C. and Gower, John (2009). Better biplots. Computational Statistics and Data Analysis, 53(8) pp. 3145–3158.

Blasius, Jörg and Gower, John C. (2005). Multivariate prediction with nonlinear principal components analysis: application. Quality and Quantity, 39(4) pp. 373–390.

de Rooij, Mark and Gower, John C. (2003). The Geometry of triadic distances. Journal of Classification, 20(2) pp. 181–220.

Denis, Jean-Baptise and Gower, John C. (1996). Asymptotic confidence regions for biadditive models: interpreting genotype-environment interactions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 45(4) pp. 479–493.

Denis, Jean-Baptiste and Gower, John C. (1994). Asymptotic covariances for the parameters of biadditive models. Utilitas Mathematica, 46 pp. 193–205.

Gower, John C.; Le Roux, Niël J. and Gardner-Lubbe, Sugnet (2022). Properties of individual differences scaling and its interpretation. Statistical Papers, 63 pp. 1221–1245.

Gower, John C.; Le Roux, Niël J. and Gardner-Lubbe, Sugnet (2016). Biplots: qualititative data. Wiley Interdisciplinary Reviews: Computational Statistics, 8(2) pp. 82–111.

Gower, John C. and Albers, Casper J. (2011). Between-group metrics. Journal of Classification, 28(3) pp. 315–326.

Gower, John C. (2010). Procrustes methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4) pp. 503–508.

Gower, J. C.; Groenen, P. J. F. and Van der Velden, M. (2010). Area biplots. Journal of Computational and Graphical Statistics, 19(1) pp. 46–61.

Gower, John (2008). The biological stimulus to multidimensional data analysis. Electronic Journal for History of Probability and Statistics, 4(2)

Gower, John (2008). Asymmetry analysis: The place of models. In: Shigemasu, K.; Okada, A.; Imaizuma, T. and Hodhina, T. eds. New Trends in Psychometrics. Universal Academy Press, pp. 69–78.

Gower, John (2006). Divided by a Common Language: Analyzing and visualizing two-way arrays. In: Greenacre, Michael and Blasius, Joerg eds. Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC Statistics in the Social and Behavioral Science (1). Boca Raton, Florida: Chapman and Hall, pp. 77–105.

Gower, J. C. (2006). Statistica data analytica est et aliter. Statistica Neerlandica, 60(2) pp. 124–134.

Gardner, Sugnet; Gower, John C. and le Roux, N.J. (2006). A Synthesis of canonical variate analysis, generalised canonical correlation and Procrustes analysis. Computational Statistics and Data Analysis, 50(1) pp. 107–134.

Gower, John C. (2006). An application of the modified Leverrier–Faddeev algorithm to the spectral decomposition of symmetric block-circulant matrices. Computational Statistics and Data Analysis, 50(1) pp. 89–106.

Gower, John and Blasius, Jörg (2005). Multivariate prediction with nonlinear principal components analysis (methodology). Quality and Quantity, 39(4) pp. 359–372.

Gower, John C. and Ngouenet, Roger F. (2005). Nonlinearity effects in multidimensional scaling. Journal of Multivariate Analysis, 94(2) pp. 344–365.

Gower, John (2005). Biplot. In: Everitt, Brian and Howell, David eds. Encyclopedia of Statistics in Behavioral Sciences, Volume 1. New York: Wiley, pp. 163–164.

Gower, J. C. (2004). The geometry of biplot scaling. Biometrika, 91(3) pp. 705–714.

Gower, J. C. (2004). Classifying classification problems. In: Comptes Rendus des Iles Rencontres de la Société Francophone de Classification (Chavent, M; Dordan, O; Lacomblez, C. C.; Langlais, M and Patouille, B eds.), pp. 41–47.

Gower, John C. and Dijksterhuis, Garmt B. (2004). Procrustes problems. Oxford Statistical Science Series, 30. Oxford, UK: Oxford University Press.

Gower, John (2003). Visualisation in multivariate and multidimensional data analysis. In: Bulletin of the International Statistical Institute, Information not supplied, pp. 101–104.

Gower, John C. and de Rooij, Mark (2003). A Comparison of the multidimensional scaling of triadic and dyadic distances. Journal of Classification, 20(1) pp. 115–136.

Gower, John (2003). Unified biplot geometry. metodološki zvezki (Develpments in Applied Statistics), 19 pp. 3–22.

Gower, J. C. (2001). Procrustes analysis. In: Smelser, N. J. and Baltes, P. B. eds. International Encyclodpedia of the Social and Behavioural Sciences, Volume 18. Oxford: Elsevier, pp. 12141–12143.

Gower, John C. (2000). Rank-one and rank-two departures from symmetry. Computational Statistics and Data Analysis, 33(2) pp. 177–188.

Gower, John (1999). Geometry, algebra and data analysis. The Mathematical Scientist, 24(2) pp. 75–89.

Gower, J. C.; Meulman, J. J. and Arnold, G. M. (1999). Non-metric linear biplots. Journal of Classification, 16(2) pp. 181–196.

Gower, J. C. and Krzanowski, W. J. (1999). Analysis of distance for structured multi-variate data. Journal of the Royal Statistical Society. Series C (Applied Statistics), 48(4) pp. 505–519.

Gower, John (1999). Discussion of contrast between psycometric and statistical approaches to multiway data analysis. In: International Statistical Insitute ed. Bulletin of the International Statistical Institute, Volume 58. Helsinki: International Statistical Institute, pp. 101–102.

Gower, J. C. (1998). The role of constraints in determining optimal scores. Statistics in Medicine, 17(23) pp. 2709–2721.

Gower, J. C. and Zielman, B. (1998). Orthogonality and its approximation in the analysis of asymmetry. Linear Algebra and its Applications, 278(1-3) pp. 183–193.

Gower, John (1998). Classification, overview. In: Armitage, P. and Coulton, Theodore eds. Encyclopedia of Biostatistics, Volume 1. John Wiley and Sons, Inc., pp. 656–667.

Gower, John (1998). Principal coordinates analysis. In: Armitage, P. and Coulton, Theodore eds. Encyclopedia of Biostatistics, Volume 5. John Wiley and Sons, Inc., pp. 3514–3518.

Gower, John (1998). Similarity, dissimilarity, and distance measure. In: Armitage, P. and Coulton, Theodore eds. Encyclopedia of Biostatistics, Volume 5. John Wiley and Sons, Inc., pp. 4097–4100.

Gower, John and Harding, Simon A. (1998). Prediction regions for categorical variables. In: Blasius, Jörg and Greenacre, Michael eds. Visualization of Categorical Data. Elsevier Inc., pp. 405–419.

Gower, J. C. and Ngouenet, R. F (1998). Some new types of biplot. In: Proceeding of the Fourth Sensometrics (Brockhoff, P.B ed.), pp. 60–63.

Gower, John C. and Ross, Gavin J. S. (1998). Non-probabilistic classification. In: Rizzi, Alfredo; Vichi, Maurizio and Bock, Hans Hermann eds. Advances in Data Science and Classification. New York, NY U.S.: Springer, pp. 21–28.

Gower, John; Hand, D. J. and Ngouenet, R. F. (1997). The development of interactive graphical software for a unified biplot methodology. In: Wegman, E. J. and Azen, S. P. eds. Computing Science and Statistics, Volume 29. International Association for Statistical Computing, pp. 22–27.

Gower, John (1996). Multivariate and multidimensional analysis. In: Armitage, Peter and David, Herbert A. eds. Advances in Biometry. Wiley Series in Probability and Statistics. New York, U.S.: Wiley.

Gower, John C. and Greenacre, Michael J. (1996). Unfolding a symmetric matrix. Journal of Classification, 13(1) pp. 81–105.

Gower, J. C. and Hand, David J. (1995). Biplots. Monographs on statistics and applied probability, 54. London, U.K.: Chapman & Hall.

Gower, John (1995). A general theory of biplots. In: Krzanowski, W.J. ed. Recent Advances in Descriptive Multivariate Statistics. Royal Statistical Society Lecture Notes, 2. Oxford, U.K.: Oxford University Press, pp. 283–303.

Gower, John (1995). Orthogonal and projection procrustes analysis. In: Krzanowski, W.J. ed. Recent Advances in Descriptive Multivariate Statistics. Royal Statistical Society Lecture Notes, 2. Oxford, U.K.: Oxford University Press, pp. 113–134.

Gower, John (1995). Distance-geometry and shape. In: Proceedings in Current Issues in Statistical Shape Analysis, 5-7 Apr 1995, Leeds, U.K..

Gower, John C. and Dijksterhuis, Garmt B. (1994). Coffee images: a study in the simultaneous display of multivariate quantitative and categroical variables for several assessors. Quality and Quantity, 28(2) pp. 165–184.

Lubbe-Gardner, Sugnet; le Roux, Niël J. and Gower, John C. (2008). Measures of fit in principal component and canonical variate analysis. Journal of Applied Statistics, 35(9) pp. 947–965.

Wieringa, Jaap; Dijksterhuis, Garmt; Gower, John and van Perlo, Frederieke (2009). Generalised procrustes analysis with optimal scaling: exploring data from a power supplier. Computational Statistics and Data Analysis, 53(12) pp. 4546–4554.

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