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Significance levels in exploratory projection pursuit. (English) Zbl 0753.62067

Summary: In exploratory data analysis, projection pursuit methods explore the ‘nonlinear’ structure of high dimensional data. It is useful to have a significance test to help us decide whether apparent structure is real or just the effect of noise. Monte Carlo methods can be helpful to achieve this, but, unfortunately, in this case they are computationally expensive.
Under a suitable null hypothesis, we derive a theoretical approximation for the \(P\) value associated with Friedman’s projection pursuit index [J. H. Friedman, J. Am. Stat. Assoc. 82, 249-266 (1987; Zbl 0664.62060)]. The result of Monte Carlo simulations is compared with our analytical result. Some practical aspects are discussed.

MSC:

62M40 Random fields; image analysis
62H99 Multivariate analysis

Citations:

Zbl 0664.62060
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