Estimation of the extremal index using censored distributions

The extremal index is an important parameter in the characterization of extreme values of a stationary sequence, since it measures short-range dependence at extreme values and governs clustering of extremes. This paper presents a novel approach to estimation of the extremal index based on artificial censoring of inter-exceedance times. The censored estimator based on the maximum likelihood method is derived together with its variance, which is estimated from the expected Fisher information measure. In order to evaluate performance of the proposed estimator, a simulation study is carried out for various stationary processes satisfying the local dependence condition $D^{(k)}(u_n)$. An application to daily maximum temperatures at Uccle, Belgium, is also presented.