Computationally Intensive Techniques
It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This exposure to computational methods is of an essential nature when we consider extremely high-dimensional data. Computer-aided techniques can help us to discover dependencies in high dimensions without complicated mathematical tools.
- L. Breiman, J.H. Friedman, R. Olshen, C.J. Stone, Classification and Regression Trees (Wadsworth, Belmont, 1984)Google Scholar
- EUNITE. Electricity load forecast competition of the European Network on Intelligent Technologies for Smart Adaptive Systems (2001). http://neuron.tuke.sk/competition/
- J.H. Friedman, W. Stuetzle, Projection Pursuit Classification (1981). (Unpublished manuscript)Google Scholar
- K. Kendall, S. Stuart, Distribution Theory, vol. 1, The Advanced Theory of Statistics (Griffin, London, 1977)Google Scholar
- J.B. Kruskal, Toward a practical method which helps uncover the structure of a set of observations by finding the line tranformation which optimizes a new “index of condensation”, in Statistical Computation, ed. by R.C. Milton, J.A. Nelder (Academic Press, New York, 1969), pp. 427–440Google Scholar
- J.B. Kruskal, Linear transformation of multivariate data to reveal clustering, in Multidimensional Scaling: Theory and Applications in the Behavioural Sciences, volume 1, ed. by R.N. Shepard, A.K. Romney, S.B. Nerlove (Seminar Press, London, 1972), pp. 179–191Google Scholar