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On robust estimation via pseudo-additive information

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  • Davide Ferrari
  • Davide La Vecchia
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    Abstract

    We consider a robust parameter estimator minimizing an empirical approximation to the q-entropy and show its relationship to minimization of power divergences through a simple parameter transformation. The estimator balances robustness and efficiency through a tuning constant q and avoids kernel density smoothing. We derive an upper bound to the estimator mean squared error under a contaminated reference model and use it as a min-max criterion for selecting q. Copyright 2012, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/biomet/asr061
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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 99 (2012)
    Issue (Month): 1 ()
    Pages: 238-244

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    Handle: RePEc:oup:biomet:v:99:y:2012:i:1:p:238-244

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    Cited by:
    1. Chao Huang & Jin-Guan Lin & Yan-Yan Ren, 2013. "Testing for the shape parameter of generalized extreme value distribution based on the $$L_q$$ -likelihood ratio statistic," Metrika, Springer, vol. 76(5), pages 641-671, July.

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