Projection Pursuit Through φ-Divergence Minimisation

Entropy

关键词: projection pursuit;    minimum φ-divergence;    elliptical distribution;    goodness-of-fit;    copula;    regression;   

DOI  :  10.3390/e12061581

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2010 by the author; licensee MDPI, Basel, Switzerland.