Robust tests based on dual divergence estimators and saddlepoint approximations

Authors:
Aida Toma;Samuela Leoni-Aubin
Affiliations:
Mathematics Department, Academy of Economic Studies, Piaa Roman 6, Bucharest, Romania and "Gheorghe Mihoc - Caius Iacob" Institute of Mathematical Statistics and Applied Mathematics, Calea 13 Sept ...;INSA Lyon, ICJ, 20, Rue Albert Einstein, 69621 Villeurbanne Cedex, France
Venue:
Journal of Multivariate Analysis
Year:
2010

Citing 2
Cited 2

Between stability and higher-order asymptotics

Statistics and Computing
Parametric estimation and tests through divergences and the duality technique

Journal of Multivariate Analysis

Dual divergence estimators and tests: Robustness results

Journal of Multivariate Analysis
Optimal robust M-estimators using Rényi pseudodistances

Journal of Multivariate Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is devoted to robust hypothesis testing based on saddlepoint approximations in the framework of general parametric models. As is known, two main problems can arise when using classical tests. First, the models are approximations of reality and slight deviations from them can lead to unreliable results when using classical tests based on these models. Then, even if a model is correctly chosen, the classical tests are based on first order asymptotic theory. This can lead to inaccurate p-values when the sample size is moderate or small. To overcome these problems, robust tests based on dual divergence estimators and saddlepoint approximations, with good performances in small samples, are proposed.