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A Semi-Parametric KDE-GPD Model for Earthquake Magnitude Analysis

Author

Listed:
  • Yanfang Zhang

    (College of Science, Institute of Disaster Prevention, Langfang 065201, China)

  • Yibin Zhao

    (College of Science, Institute of Disaster Prevention, Langfang 065201, China)

  • Fuchang Wang

    (College of Science, Institute of Disaster Prevention, Langfang 065201, China)

Abstract

A semi-parametric mixture model, combining kernel density estimation (KDE) and the generalized Pareto distribution (GPD), is applied to analyze the statistical characteristics of earthquake magnitudes. Data below a threshold are fitted using KDE, while data above the threshold are modeled using the GPD. Both the kernel bandwidth and the threshold are directly estimable as parameters. An estimation method based on the empirical distribution function (EDF) and maximum likelihood estimation (MLE) is used to estimate the parameters of the mixture model. The application of this model to earthquake magnitude analysis offers insights for seismic hazard assessment.

Suggested Citation

  • Yanfang Zhang & Yibin Zhao & Fuchang Wang, 2025. "A Semi-Parametric KDE-GPD Model for Earthquake Magnitude Analysis," Mathematics, MDPI, vol. 13(12), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:2003-:d:1681259
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    References listed on IDEAS

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