Abstract
Projection Pursuit methodology permits to solve the difficult problem of finding an estimate of a density defined on a set of very large dimension. In his seminal article, “Projection Pursuit”, Huber (Citation1985) evidenced the interest of the Projection Pursuit method thanks to the factorization of a density into a Gaussian component and some residual density in a context of Kullback–Leibler divergence maximisation.
In the present article, we introduce a new algorithm, and in particular, a test for the factorisation of a density estimated from an iid sample.
Notes
Therefore, we conclude that f = g (2).
Therefore, we conclude that f = g (1).
Therefore, we conclude that f = g (1).