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.
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Adimari, G., Ventura, L. Quasi-likelihood fromM-estimators: A numerical comparison with empirical likelihood. Statistical Methods & Applications 11, 175–185 (2002). https://doi.org/10.1007/BF02511485
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DOI: https://doi.org/10.1007/BF02511485