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Quasi-likelihood fromM-estimators: A numerical comparison with empirical likelihood

Author

Listed:
  • Gianfranco Adimari

    (Università degli Studi di Padova)

  • Laura Ventura

    (Università degli Studi di Padova)

Abstract

In this paper we compare two robust pseudo-likelihoods for a parameter of interest, also in the presence of nuisance parameters. These functions are obtained by computing quasi-likelihood and empirical likelihood from the estimating equations which define robustM-estimators. Application examples in the context of linear transformation models are considered. Monte Carlo studies are performed in order to assess the finite-sample performance of the inferential procedures based on quasi-and empirical likelihood, when the objective is the construction of robust confidence regions.

Suggested Citation

  • Gianfranco Adimari & Laura Ventura, 2002. "Quasi-likelihood fromM-estimators: A numerical comparison with empirical likelihood," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 175-185, June.
    • Handle: RePEc:spr:stmapp:v:11:y:2002:i:2:d:10.1007_bf02511485
    DOI: 10.1007/BF02511485
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    References listed on IDEAS

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    1. O. Barndorff-Nielsen, 1995. "Quasi profile and directed likelihoods from estimating functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(3), pages 461-464, September.
    2. Adimari, Gianfranco & Ventura, Laura, 2001. "Robust inference for generalized linear models with application to logistic regression," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 413-419, December.
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    Cited by:

    1. Luca Greco & Laura Ventura, 2011. "Robust inference for the stress–strength reliability," Statistical Papers, Springer, vol. 52(4), pages 773-788, November.

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