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On robust estimation via pseudo-additive information


  • Davide Ferrari
  • Davide La Vecchia


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.

Suggested Citation

  • Davide Ferrari & Davide La Vecchia, 2012. "On robust estimation via pseudo-additive information," Biometrika, Biometrika Trust, vol. 99(1), pages 238-244.
  • Handle: RePEc:oup:biomet:v:99:y:2012:i:1:p:238-244

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    References listed on IDEAS

    1. Joseph P. Romano & Michael Wolf, 2005. "Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 94-108, March.
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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: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(5), pages 641-671, July.
    2. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    3. Giuzio, Margherita & Ferrari, Davide & Paterlini, Sandra, 2016. "Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 251-261.
    4. Davide La Vecchia, 2016. "Stable Asymptotics for M-estimators," International Statistical Review, International Statistical Institute, vol. 84(2), pages 267-290, August.

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