Dual divergence estimators and tests: robustness results

Aida Toma (1) , Michel Broniatowski (2)

1 Mathematics Department
2 LSTA - Laboratoire de Statistique Théorique et Appliquée

The class of dual $\phi$-divergence estimators (introduced in Broniatowski and Keziou (2009) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hypothesis tests based on dual divergence criterions are proposed and their robustness properties are studied. The empirical performances of these estimators and tests are illustrated by Monte Carlo simulation for both noncontaminated and contaminated data.

Mots clés: robust estimation; robust test; location model; scale model; Minimum divergence estimators

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Aida Toma, Michel Broniatowski. Dual divergence estimators and tests: robustness results. Journal of Multivariate Analysis, 2011, 102 (1), pp.20-36. ⟨hal-00441124v2⟩