Robust and bias-corrected estimation of the coefficient of tail dependence
نویسندگان
چکیده
We introduce a robust and asymptotically unbiased estimator for the coefficient of tail dependence in multivariate extreme value statistics. The estimator is obtained by fitting a second order model to the data by means of the minimum density power divergence criterion. The asymptotic properties of the estimator are investigated. The efficiency of our methodology is illustrated on a small simulation study and by a real dataset from the actuarial context.
منابع مشابه
The analysis of residuals variation and outliers to obtain robust response surface
In this paper, the main idea is to compute the robust regression model, derived by experimentation, in order to achieve a model with minimum effects of outliers and fixed variation among different experimental runs. Both outliers and nonequality of residual variation can affect the response surface parameter estimation. The common way to estimate the regression model coefficients is the ordinar...
متن کاملNonparametric Estimation of the Tail-dependence Coefficient
• A common measure of tail dependence is the so-called tail-dependence coefficient. We present a nonparametric estimator of the tail-dependence coefficient and prove its strong consistency and asymptotic normality in the case of known marginal distribution functions. The finite-sample behavior as well as robustness will be assessed through simulation. Although it has a good performance, it is s...
متن کاملInvestigation of the Allometric Models in Estimation of Poplar (Populus deltoides) Height
One of the most important issues in forest biometrics is the use of allometric functions to estimate the tree height by using diameter-height models. Measuring the total height of trees is usually a complex and time-consuming process. In allometric functions, the diameter is measured directly but the height of the tree is an estimate of an allometric model, which will be more accurate if the cr...
متن کامل2 0 Fe b 20 02 Tail Dependence of Factor Models ∗
Using the framework of factor models, we establish the general expression of the coefficient of tail dependence between the market and a stock (i.e., the probability that the stock incurs a large loss, assuming that the market has also undergone a large loss) as a function of the parameters of the underlying factor model and of the tail parameters of the distributions of the factor and of the i...
متن کاملDetection of Outliers and Influential Observations in Linear Ridge Measurement Error Models with Stochastic Linear Restrictions
The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...
متن کامل