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Statistical Papers

, Volume 57, Issue 4, pp 957–976 | Cite as

Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function

  • Simos G. MeintanisEmail author
  • James Allison
  • Leonard Santana
Regular Article

Abstract

We investigate the finite-sample properties of certain procedures which employ the novel notion of the probability weighted empirical characteristic function. The procedures considered are: (1) Testing for symmetry in regression, (2) Testing for multivariate normality with independent observations, and (3) Testing for multivariate normality of random effects in mixed models. Along with the new tests alternative methods based on the ordinary empirical characteristic function as well as other more well known procedures are implemented for the purpose of comparison.

Keywords

Characteristic function Empirical characteristic function Goodness-of-fit test Mixed model Multivariate normal distribution Test for symmetry 

Mathematics Subject Classification

62F03 62G10 62H15 

Notes

Acknowledgments

S. Meintanis acknowledges support by the Special Account for Research Grants (ELKE) (Research Grant 11699) of the National & Kapodistrian University of Athens. J. Allison thanks the National Research Foundation of South Africa for financial support.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Simos G. Meintanis
    • 1
    • 2
    Email author
  • James Allison
    • 2
  • Leonard Santana
    • 2
  1. 1.Department of EconomicsNational and Kapodistrian University of AthensAthensGreece
  2. 2.Unit for Business Mathematics and InformaticsNorth-West UniversityPotchefstroomSouth Africa

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