An Estimator of Heavy Tail Index through the Generalized Jackknife Methodology

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ID: 13463
2014
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Abstract
In practice, sometimes the data can be divided into several blocks but only a few of the largest observations within each block are available to estimate the heavy tail index. To address this problem, we propose a new class of estimators through the Generalized Jackknife methodology based on Qi’s estimator (2010). These estimators are proved to be asymptotically normal under suitable conditions. Compared to Hill’s estimator and Qi’s estimator, our new estimator has better asymptotic efficiency in terms of the minimum mean squared error, for a wide range of the second order shape parameters. For the finite samples, our new estimator still compares favorably to Hill’s estimator and Qi’s estimator, providing stable sample paths as a function of the number of dividing the sample into blocks, smaller estimation bias, and MSE.
Reference Key
liu2014anmathematical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Weiqi;Xing, Hongwei;
Journal mathematical problems in engineering
Year 2014
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2014/686240
Keywords Keywords not found

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