On the relationship between empirical likelihood and empirical saddlepoint approximation for multivariate M-estimators
|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 likelihood — Empirical likelihood — Influence function — M-estimator — Nonparametric confidence regions — Saddlepoint approximation — Small 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|