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Multivariate density estimation using dimension reducing information and tail flattening transformations

By Tine Buch-Kromann, Montserrat Guillén, Oliver Linton and Jens Perch Nielsen


We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding a tail flattening transformation improves the estimation significantly-particularly in the tail-and provides significant graphical advantages by allowing the density estimation to be visualized in a simple way. The combined method is demonstrated on a fire insurance data set and in a data-driven simulation study. © 2010 Elsevier B.V

Topics: HB Economic Theory, QA Mathematics
Publisher: Elsevier B V
Year: 2011
DOI identifier: 10.1016/j.insmatheco.2010.10.002
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Provided by: LSE Research Online
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