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A family of non-parametric tests for the class of log-symmetric distributions

Author

Listed:
  • Ganesh Vishnu Avhad
  • Ananya Lahiri
  • Sudheesh K. Kattumannil

Abstract

The continuous, strictly positive, and asymmetric variables, which may include outliers, are commonly encountered across various fields. Log-symmetric distributions are frequently useful for modeling such data. The characterization properties of these distributions are employed to construct the goodness of fit. In this paper, we propose a new goodness of fit test tailored for log-symmetric distributions. We develop the test based on the jackknife empirical likelihood and the adjusted jackknife empirical likelihood. The asymptotic distribution of both test statistics is shown to follow a chi-square distribution with one degree of freedom. The performance of the proposed tests and their comparison with existing methods are evaluated through a comprehensive Monte Carlo simulations study. The proposed methods perform better than other methods. Finally, the method is illustrated with applications to real-world datasets.

Suggested Citation

  • Ganesh Vishnu Avhad & Ananya Lahiri & Sudheesh K. Kattumannil, 2026. "A family of non-parametric tests for the class of log-symmetric distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 53(1), pages 68-83, January.
    • Handle: RePEc:taf:japsta:v:53:y:2026:i:1:p:68-83
    DOI: 10.1080/02664763.2025.2503856
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