Random Average Shifted Histograms

Mathias Bourel , Badih Ghattas (1) , Fraiman Ricardo (2)

1 I2M - Institut de Mathématiques de Marseille
2 CMAT - Centro de Matemática [Montevideo]

A new density estimator called RASH, for Random Average Shifted Histogram, obtained by averaging several histograms as proposed in average shifted histograms, is presented. The principal difference between the two methods is that in RASH each histogram is built over a grid with random shifted breakpoints. The asymptotic behavior of this estimator is established for the one-dimensional case and its performance through several simulations is analyzed. RASH is compared to several classic density estimators and to some recent ensemble methods. Although RASH does not always outperform the other methods, it is very simple to implement, being also more intuitive. The two dimensional case is also analyzed empirically.

Mots clés: Machine learning; Histogram; Density estimation; Bootstrap; Bagging; Average shifted histograms

Domaines: Statistiques [stat]

Mathias Bourel, Badih Ghattas, Fraiman Ricardo. Random Average Shifted Histograms. Computational Statistics and Data Analysis, 2014, ⟨10.1016/j.csda.2014.05.004⟩. ⟨hal-01266312⟩