DOI: https://doi.org/10.14710/j.gauss.12.3.392-402

PENGGUNAAN MIXTURE MODEL KERNEL-GENERALIZED PARETO DISTRIBUTION DAN D-VINE COPULA DALAM MENGANALISIS UKURAN PELANGGARAN DATA

*Fathiyyah Yolianda Dzikra  -  Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Yuciana Wilandari  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Arief Rachman Hakim  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

How to cite (IEEE): F. Y. Dzikra, Y. Wilandari, and A. R. Hakim, "PENGGUNAAN MIXTURE MODEL KERNEL-GENERALIZED PARETO DISTRIBUTION DAN D-VINE COPULA DALAM MENGANALISIS UKURAN PELANGGARAN DATA," Jurnal Gaussian, vol. 12, no. 3, pp. 392-402, Feb. 2024. https://doi.org/10.14710/j.gauss.12.3.392-402
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Abstract
The research conducted on the 2015-2021 Data Breach Report in the U.S. Department of Health and Human Services is a study related to the estimation and modeling of the breach sizes each type of entity using the Kernel-Generalized Pareto Distribution Mixture Model method, as well as the estimation of the dependence of breach sizes between years with the D-Vine Copula. The D-Vine Copula can accommodate the complex dependencies demonstrated by data breach reports across all enterprise categories. Before researching with D-Vine Copula, we will first model and estimate breach size parameters for each type of entity using the Mixture Model Kernel-Generalized Pareto Distribution (GPD). The Mixture Model can accommodate large data breach sizes via GPD and also allows the use of non-parametric kernel distributions to model smaller data breach sizes. The data resulting from the logarithmic transformation of entity data in the Business Associate and Healthcare Provider types has a right short-tail with Weibull distribution, while the Health Plan category has a right heavy-tail with Frechet distribution. The three types of entity were estimated using the maximum likelihood Cross-Validation method. Dependency estimation with D-Vine Copula shows that the breach sizes between years measure has a positive dependency.
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Keywords: Breach Sizes; Generalized Pareto Distribution; Kernel; D-Vine Copula

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