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Estimation in the Generalized Poisson Model via Robust Testing

In: Developments in Robust Statistics

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  • T. Bednarski

    (Technical University, Institute of Mathematics)

Abstract

Summary An estimation method is presented which compromises robust efficiency with computational feasibility in the case of the generalized Poisson model. The formal setup is built on flexible nonparametric extensions of the underlying model. The estimation efficiency is expressed via minimax properties of tests resulting from expansions of estimators. The non-parametric neighborhoods related to the proposed score function are exemplified and a real data case is analysed. The resulting method balances several qualitative features of statistical inference: strong differentiability (asymptotic derivations are more accurate), efficiency and natural model extension (quality of formal basic assumptions).

Suggested Citation

  • T. Bednarski, 2003. "Estimation in the Generalized Poisson Model via Robust Testing," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 88-97, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_7
    DOI: 10.1007/978-3-642-57338-5_7
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