Quasi-likelihood fromM-estimators: A numerical comparison with empirical likelihood
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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.
Key wordsEstimating equation linear transformation models profile likelihood pseudo likelihood robustness
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- Adimari G, Ventura L (2002) Quasi-profile loglikelihoods for unbiased estimating functions.Ann. Inst. Statist. Math., to appearGoogle Scholar
- Bellio R, Brazzale AR, Ventura L (2001) Adjusted quasi-profile likelihoods and robustness. In: Proceedings of the 16th international workshop on statistical modelling, Odense Denmark, July 2–6 2001: 413–416Google Scholar
- Maronna RA, Bustos O, Yohai VJ (1979) Bias- and efficiency-robustness of general M-estimators for regression with random carriers. In: Gasser T, Rosenblatt M (eds) Smoothing techniques for curve estimation. Lecture Notes in Mathematics 757, pp. 91–116. Springer, Berlin Heidelberg New YorkGoogle Scholar
- McCullagh P (1991) Quasi-likelihood and estimating functions. In: Hinkley DV, Reid N, Snell EJ (eds) Statistical theory and modelling, pp. 265–286. Chapman and Hall, LondonGoogle Scholar
- Owen AB (1991) Empirical likelihood for linear models.Ann. Statist. 22: 300–325Google Scholar