Entropy 2010, 12(6), 1581-1611; doi:10.3390/e12061581

Projection Pursuit Through ϕ-Divergence Minimisation

Received: 8 April 2010; in revised form: 27 May 2010 / Accepted: 31 May 2010 / Published: 14 June 2010
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: In his 1985 article (“Projection pursuit”), Huber demonstrates the interest of his method to estimate a density from a data set in a simple given case. He considers the factorization of density through a Gaussian component and some residual density. Huber’s work is based on maximizing Kullback–Leibler divergence. Our proposal leads to a new algorithm. Furthermore, we will also consider the case when the density to be factorized is estimated from an i.i.d. sample. We will then propose a test for the factorization of the estimated density. Applications include a new test of fit pertaining to the elliptical copulas.
Keywords: projection pursuit; minimum ϕ-divergence; elliptical distribution; goodness-of-fit; copula; regression
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MDPI and ACS Style

Touboul, J. Projection Pursuit Through ϕ-Divergence Minimisation. Entropy 2010, 12, 1581-1611.

AMA Style

Touboul J. Projection Pursuit Through ϕ-Divergence Minimisation. Entropy. 2010; 12(6):1581-1611.

Chicago/Turabian Style

Touboul, Jacques. 2010. "Projection Pursuit Through ϕ-Divergence Minimisation." Entropy 12, no. 6: 1581-1611.

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