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Transformations to symmetry based on the probability weighted characteristic function

Author

Listed:
  • Simos G. Meintanis
  • Gilles Stupfler

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We suggest a nonparametric version of the probability weighted empirical characteristic function (PWECF) introduced by Meintanis \et al.\ \\cite\meiswaall2014\ 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.

Suggested Citation

  • Simos G. Meintanis & Gilles Stupfler, 2015. "Transformations to symmetry based on the probability weighted characteristic function," Post-Print hal-01457397, HAL.
  • Handle: RePEc:hal:journl:hal-01457397
    DOI: 10.14736/kyb-2015-4-0571
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    Cited by:

    1. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Simos G. Meintanis & James Allison & Leonard Santana, 2016. "Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function," Statistical Papers, Springer, vol. 57(4), pages 957-976, December.

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    Keywords

    Economie quantitative;

    Statistics

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