Transformations to symmetry based on the probability weighted characteristic function

Simos G. Meintanis; Gilles Stupfler

Kybernetika (2015)

  • Volume: 51, Issue: 4, page 571-587
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Meintanis, Simos G., and Stupfler, Gilles. "Transformations to symmetry based on the probability weighted characteristic function." Kybernetika 51.4 (2015): 571-587. <http://eudml.org/doc/271816>.

@article{Meintanis2015,
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.},
author = {Meintanis, Simos G., Stupfler, Gilles},
journal = {Kybernetika},
keywords = {characteristic function; empirical characteristic function; probability weighted moments; symmetry transformation; characteristic function; empirical characteristic function; probability weighted moments; symmetry transformation},
language = {eng},
number = {4},
pages = {571-587},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Transformations to symmetry based on the probability weighted characteristic function},
url = {http://eudml.org/doc/271816},
volume = {51},
year = {2015},
}

TY - JOUR
AU - Meintanis, Simos G.
AU - Stupfler, Gilles
TI - Transformations to symmetry based on the probability weighted characteristic function
JO - Kybernetika
PY - 2015
PB - Institute of Information Theory and Automation AS CR
VL - 51
IS - 4
SP - 571
EP - 587
AB - 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.
LA - eng
KW - characteristic function; empirical characteristic function; probability weighted moments; symmetry transformation; characteristic function; empirical characteristic function; probability weighted moments; symmetry transformation
UR - http://eudml.org/doc/271816
ER -

References

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