Search

We propose an estimator of the conditional tail moment (CTM) when the data are subject to random censorship. The variable of main interest and the censoring variable both follow a Pareto-type distribution. We establish the asymptotic properties of our estimator and discuss bias-reduction. Then, the CTM is used to estimate, in case of censorship, the premium principle for excess-of-loss reinsurance. The finite sample properties of the proposed estimators are investigated with a simulation study and we illustrate their practical applicability on a dataset of motor third party liability insurance. [ABSTRACT FROM AUTHOR]

The stable tail dependence function gives a full characterisation of the extremal dependence between two or more random variables. In this paper, we propose an estimator for this function which is robust against outliers in the sample. The estimator is derived from a bivariate second-order tail model together with a proper transformation of the bivariate observations, and its asymptotic properties are studied under some suitable regularity conditions. Our estimation procedure depends on two parameters: $$\alpha $$ , which controls the trade-off between efficiency and robustness of the estimator, and a second-order parameter $$\tau $$ , which can be replaced by a fixed value or by an estimate. In case where $$\tau $$ has been replaced by the true value or by an external consistent estimator, our robust estimator is asymptotically unbiased, whereas in case where $$\tau $$ is mis-specified, one loses this property, but still our estimator performs quite well with respect to bias. The finite sample performance of our robust and bias-corrected estimator of the stable tail dependence function is examined on a simulation study involving uncontaminated and contaminated samples. In particular, its behavior is illustrated for different values of the pair $$(\alpha , \tau )$$ and is compared with alternative estimators from the extreme value literature. [ABSTRACT FROM AUTHOR]

We present a new method for estimating the endpoint of a unidimensional sample when the distribution function decreases at a polynomial rate to zero in the neighborhood of the endpoint. The estimator is based on the use of high-order moments of the variable of interest. It is assumed that the order of the moments goes to infinity, and we give conditions on its rate of divergence to get the asymptotic normality of the estimator. The good performance of the estimator is illustrated on some finite sample situations. [ABSTRACT FROM AUTHOR]

new class of estimators for the Weibull-tail coefficient is proposed. The estimators are based on linear combinations of log-spacings of the mean excess function evaluated at high levels. The asymptotic distribution of this new class of estimators is derived under some mild conditions on the weight function and a second order condition on the tail behavior. The finite sample properties of some estimators obtained with important special cases of the weight function are examined with a small simulation experiment. [ABSTRACT FROM AUTHOR]