| dc.contributor.author | Meintanis, Simos G. | |
| dc.contributor.author | Stupfler, Gilles | |
| dc.date.accessioned | 2016-09-06T06:55:42Z | |
| dc.date.available | 2016-09-06T06:55:42Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | Meintanis, S.G. & Stupfler, G. 2015. Transformations to symmetry based on the probability weighted characteristic function. Kybernetika, 51(4):571-587. [http://dx.doi.org/10.14736/kyb-2015-4-0571] | en_US |
| dc.identifier.issn | 0023-5954 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/10394/18548 | |
| dc.identifier.uri | http://dx.doi.org/10.14736/kyb-2015-4-0571 | |
| dc.identifier.uri | http://www.kybernetika.cz/content/2015/4/571 | |
| dc.description.abstract | We suggest a nonparametric version of the probability weighted empirical characteristic
function (PWECF) introduced by Meintanis et al. [10] and use this PWECF in order to estimate
the parameters of arbitrary transformations to symmetry. The almost sure consistency of the
resulting estimators is shown. Finite{sample results for i.i.d. data are presented and are
subsequently extended to the regression setting. A real data illustration is also included | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Information Theory and Automation | en_US |
| dc.subject | Characteristic function | en_US |
| dc.subject | empirical characteristic function | en_US |
| dc.subject | probability weighted moments | en_US |
| dc.subject | symmetry transformation | en_US |
| dc.title | Transformations to symmetry based on the probability weighted characteristic function | en_US |
| dc.type | Article | en_US |
| dc.contributor.researchID | 21262977 - Meintanis, Simos George | |
