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Multivariate density estimation using dimension reducing information and tail flattening transformations

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

Abstract

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.

Suggested Citation

  • Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
  • Handle: RePEc:eee:insuma:v:48:y:2011:i:1:p:99-110
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ramon Alemany & Catalina Bolancé & Montserrat Guillén, 2012. "Nonparametric estimation of Value-at-Risk," Working Papers XREAP2012-19, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2012.
    2. Urbina, Jilber & Guillén, Montserrat, 2013. "An application of capital allocation principles to operational risk," Working Papers 2072/222201, Universitat Rovira i Virgili, Department of Economics.
    3. Gámiz Pérez, M. Luz & Martínez Miranda, María Dolores & Nielsen, Jens Perch, 2013. "Smoothing survival densities in practice," Computational Statistics & Data Analysis, Elsevier, pages 368-382.
    4. Del Brio, Esther B. & Perote, Javier, 2012. "Gram–Charlier densities: Maximum likelihood versus the method of moments," Insurance: Mathematics and Economics, Elsevier, pages 531-537.

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