Inference for heavy tailed distributions

DOI10.1016/S0378-3758(97)00077-3zbMath0921.62056MaRDI QIDQ1378778

Wei Wu, Soumendra Nath Lahiri, Krishna B. Athreya

Publication date: 5 October 1999

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

bootstrapconsistencyasymptotic normalitystable lawsheavy tailed distributions

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Nonparametric tolerance and confidence regions (62G15) Order statistics; empirical distribution functions (62G30) Nonparametric statistical resampling methods (62G09)

Related Items (4)

Bootstrap inference for a class of non-regular estimators ⋮ LINK OF MOMENTS BEFORE AND AFTER TRANSFORMATIONS, WITH AN APPLICATION TO RESAMPLING FROM FAT-TAILED DISTRIBUTIONS ⋮ Bootstrapping the mean vector for the observations in the domain of attraction of a multivariate stable law ⋮ Estimation of the index parameter for autoregressive data using the estimated innovations

Cites Work

• Best attainable rates of convergence for estimates of parameters of regular variation
• Kernel estimates of the tail index of a distribution
• Limit theorems on order statistics
• Some results on the influence of extremes on the bootstrap
• A simple general approach to inference about the tail of a distribution
• Bootstrap methods: another look at the jackknife
• On the asymptotic normality of the maximum-likelihood estimate when sampling from a stable distribution
• Point processes, regular variation and weak convergence
• On the Optimality of Estimating the Tail Index and a Naive Estimator
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