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On the relationship between empirical likelihood and empirical saddlepoint approximation for multivariate M-estimators

Authors
Monti, Anna Clara
Published in Biometrika. 1993, vol. 80, no. 2, p. 329-338
Abstract By comparing the expansions of the empirical log-likelihood ratio and the empirical cumulant generating function calculated at the saddlepoint, we investigate the relationship between empirical likelihood and empirical saddlepoint approximations. This leads to a nonparametric approximation of the density of a multivariate M-estimator based on the empirical likelihood and, on the other hand, it provides nonparametric confidence regions based on the empirical cumulant generating function. Some examples illustrate the use of the empirical likelihood in saddlepoint approximations and vice versa.
Keywords Empirical bootstrap likelihoodEmpirical likelihoodInfluence functionM-estimatorNonparametric confidence regionsSaddlepoint approximationSmall sample asymptotics
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MONTI, Anna Clara, RONCHETTI, Elvezio. On the relationship between empirical likelihood and empirical saddlepoint approximation for multivariate M-estimators. In: Biometrika, 1993, vol. 80, n° 2, p. 329-338. https://archive-ouverte.unige.ch/unige:23214

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Deposited on : 2012-10-06

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