Kernel-type estimators for the extreme value index
نویسندگان
چکیده
منابع مشابه
Kernel-type Estimators for the Extreme Value Index by P. Groeneboom,
A large part of the theory of extreme value index estimation is developed for positive extreme value indices. The best-known estimator of a positive extreme value index is probably the Hill estimator. This estimator belongs to the category of moment estimators, but can also be interpreted as a quasimaximum likelihood estimator. It has been generalized to a kernel-type estimator, but this kernel...
متن کاملGeneralized Pickands estimators for the extreme value index
The Pickands estimator for the extreme value index is generalized in a way that includes all of its previously known variants. A detailed study of the asymptotic behavior of the estimators in the family serves to determine its optimally performing members. These are given by simple, explicit formulas, have the same asymptotic variance as the maximum likelihood estimator in the generalized Paret...
متن کاملEstimation for the Type-II Extreme Value Distribution Based on Progressive Type-II Censoring
In this paper, we discuss the statistical inference on the unknown parameters and reliability function of type-II extreme value (EVII) distribution when the observed data are progressively type-II censored. By applying EM algorithm, we obtain maximum likelihood estimates (MLEs). We also suggest approximate maximum likelihood estimators (AMLEs), which have explicit expressions. We provide Bayes ...
متن کاملFunctional kernel estimators of conditional extreme quantiles
We address the estimation of “extreme” conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed kernel estimators. A Weissman-type estimator and kernel estimators of the conditional tailindex are derived, permitting to estimate extreme conditio...
متن کاملMaximum likelihood estimators for the extreme value index based on the block maxima method
The maximum likelihood method offers a standard way to estimate the three parameters of a generalized extreme value (GEV) distribution. Combined with the block maxima method, it is often used in practice to assess the extreme value index and normalization constants of a distribution satisfying a first order extreme value condition, assuming implicitely that the block maxima are exactly GEV dist...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2003
ISSN: 0090-5364
DOI: 10.1214/aos/1074290333