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

Yanfang Zhang (), Yibin Zhao and Fuchang Wang
Additional contact information
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

Mathematics, 2025, vol. 13, issue 12, 1-17

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.

Keywords: KDE; mixture model; semi-parametric model; GPD; earthquake magnitude; statistical characteristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/12/2003/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/12/2003/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:12:p:2003-:d:1681259

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().