Estimating L-functionals for heavy-tailed distributions and application.
Estimating the conditional tail expectation in the case of heavy-tailed losses.
Estimation for heavy tailed moving average process
In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables innovations...
Estimation of second order parameters using probability weighted moments
The P.O.T. method (Peaks Over Threshold) consists in using the generalized Pareto distribution (GPD) as an approximation for the distribution of excesses over a high threshold. In this work, we use a refinement of this approximation in order to estimate second order parameters of the model using the method of probability-weighted moments (PWM): in particular, this leads to the introduction of a new estimator for the second order parameter ρ, which will be compared to other recent estimators through...
Estimation of second order parameters using probability weighted moments
The P.O.T. method (Peaks Over Threshold) consists in using the generalized Pareto distribution (GPD) as an approximation for the distribution of excesses over a high threshold. In this work, we use a refinement of this approximation in order to estimate second order parameters of the model using the method of probability-weighted moments (PWM): in particular, this leads to the introduction of a new estimator for the second order parameter ρ, which will be compared to other recent estimators through...
Estimation of the parameters of Gumbel and Burr distributions in terms of kth record values
The minimum variance linear unbiased estimators (MVLUE), the best linear invariant estimators (BLIE) and the maximum likelihood estimators (MLE) based on m selected kth record values are presented for the parameters of the Gumbel and Burr distributions.
Extremal behaviour of stationary processes: the calibration technique in the extremal index estimation
Classical extreme value methods were derived when the underlying process is assumed to be a sequence of independent random variables. However when observations are taken along the time and/or the space the independence is an unrealistic assumption. A parameter that arises in this situation, characterizing the degree of local dependence in the extremes of a stationary series, is the extremal index, θ. In several areas such as hydrology, telecommunications, finance and environment, for example, the...
Extremal (in)dependence of a maximum autoregressive process
Maximum autoregressive processes like MARMA (Davis and Resnick, [5] 1989) or power MARMA (Ferreira and Canto e Castro, [12] 2008) have singular joint distributions, an unrealistic feature in most applications. To overcome this pitfall, absolute continuous versions were presented in Alpuim and Athayde [2] (1990) and Ferreira and Canto e Castro [14] (2010b), respectively. We consider an extended version of absolute continuous maximum autoregressive processes that accommodates both asymptotic tail...
Extremal moment methods and stochastic orders.
Extremal moment methods and stochastic orders. Application in actuarial science.
Extremes of spheroid shape factor based on two dimensional profiles
The extremal shape factor of spheroidal particles is studied. Three dimensional particles are considered to be observed via their two dimensional profiles and the problem is to predict the extremal shape factor in a given size class. We proof the stability of the domain of attraction of the spheroid’s and its profile shape factor under a tail equivalence condition. We show namely that the Farlie–Gumbel–Morgenstern bivariate distributions gives the tail uniformity. We provide a way how to find normalising...