Empirical Likelihood based Confidence Regions for first order parameters of a heavy tailed distribution

Details Empirical Likelihood based Confidence Regions for first order parameters of a heavy tailed distribution Empirical Likelihood based Confidence Regions for first order parameters of a heavy tailed distribution

2010-08-19

arxiv.org/abs/1008.3229v1

Mathematics

Statistics Theory

Scientific paper

Let $X_1, \ldots, X_n$ be some i.i.d. observations from a heavy tailed distribution $F$, i.e. such that the common distribution of the excesses over a high threshold $u_n$ can be approximated by a Generalized Pareto Distribution $G_{\gamma,\sigma_n}$ with $\gamma >0$. This work is devoted to the problem of finding confidence regions for the couple $(\gamma,\sigma_n)$ : combining the empirical likelihood methodology with estimation equations (close but not identical to the likelihood equations) introduced by J. Zhang (Australian and New Zealand J. Stat n.49(1), 2007), asymptotically valid confidence regions for $(\gamma,\sigma_n)$ are obtained and proved to perform better than Wald-type confidence regions (especially those derived from the asymptotic normality of the maximum likelihood estimators). By profiling out the scale parameter, confidence intervals for the tail index are also derived.

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