Moment-Based Density Approximation Techniques as Applied to Heavy-tailed Distributions

John Sang Jin Kang, Serge B. Provost and Jiandong Ren

International Journal of Statistics and Probability, 2019, vol. 8, issue 3, 1

Abstract: Several advances are made in connection with the approximation and estimation of heavy-tailed distributions. It is first explained that on initially applying the Esscher transform to heavy-tailed density functions such as the Pareto, Studentt and Cauchy, said densities can be approximated by employing a certain moment-based methodology. Alternatively, density approximants can be obtained by appropriately truncating such distributions or mapping them onto finite supports. These techniques are then extended to the context of density estimation, their validity being demonstrated by means of simulation studies. As well, illustrative actuarial examples are presented.

Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.ccsenet.org/journal/index.php/ijsp/article/download/0/0/39024/40092 (application/pdf)
http://www.ccsenet.org/journal/index.php/ijsp/article/view/0/39024 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijspjl:v:8:y:2019:i:3:p:1

Access Statistics for this article

More articles in International Journal of Statistics and Probability from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().