Nonparametric tail estimation using a double bootstrap method
Computational Statistics & Data Analysis
Appendix: A primer on heavy-tailed distributions
Queueing Systems: Theory and Applications
About the asymptotic accuracy of Barron density estimates
IEEE Transactions on Information Theory
Accuracy of transformed kernel density estimates for a heavy-tailed distribution
Automation and Remote Control
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Nonparametric estimation of the density of a heavy-tailed probability distribution is investigated. The initial data are transformed to a bounded interval and the distribution density is determined by an inverse transformation of the distribution density estimate of transformed data. An adaptive data transformation is studied, in which the order of decay of the tail of the true distribution density is preserved and stable estimation of the deviation in tail index estimates is guaranteed. In classification, the empirical risk of erroneous classification by the Bayes empirical classifier is used as a measure for the quality of distribution density estimates.