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

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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.

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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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Authors and Affiliations

  1. Department of Economics, National and Kapodistrian University of Athens, Athens, Greece

    Simos G. Meintanis

  2. Unit for Business Mathematics and Informatics, North-West University, Potchefstroom, South Africa

    Simos G. Meintanis, James Allison & Leonard Santana

Authors
  1. Simos G. Meintanis
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  2. James Allison
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  3. Leonard Santana
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Correspondence to Simos G. Meintanis.

Additional information

Simos G. Meintanis—On sabbatical leave from the University of Athens.

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Meintanis, S.G., Allison, J. & Santana, L. Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function. Stat Papers 57, 957–976 (2016). https://doi.org/10.1007/s00362-016-0760-0

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  • DOI: https://doi.org/10.1007/s00362-016-0760-0

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