A0341
Title: Maximum likelihood estimation of elliptical multivariate regular variation
Authors: Moosup Kim - Keimyung University (Korea, South) [presenting]
Abstract: The focus is on the efficient estimation of the elliptical tail. Initially, the density function of the spectral measure of an elliptical distribution is derived concerning a dominating measure on the unit sphere, which consequently leads to the density function of the elliptical tail. Subsequently, a maximum likelihood estimation is proposed based on the derived density function class. The resulting maximum likelihood estimator (MLE) is proven to be consistent and asymptotically normal. Moreover, it is demonstrated that the MLE is asymptotically efficient, with the added advantage that its asymptotic covariance matrix can be feasibly estimated at a low computational cost. A simulation study and real data analysis are conducted to illustrate the efficacy of the proposed method.
