Robust tests based on dual divergence estimators and saddlepoint approximations

Aida Toma and Samuela Leoni-Aubin

Journal of Multivariate Analysis, 2010, vol. 101, issue 5, 1143-1155

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.

Keywords: Robust; testing; Saddlepoint; approximations; Divergences; M-estimators (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (12)

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