arXiv Open Access 2024

Improving Insurance Catastrophic Data with Resampling and GAN Methods

Norbert Dzadz Maciej Romaniuk

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Abstrak

The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute errors (MAE). Then, a direct algorithm to construct a fuzzy expert's opinion concerning such outputs is also considered.

Topik & Kata Kunci

cs.LG stat.AP stat.CO stat.ML

Penulis (2)

Norbert Dzadz

Maciej Romaniuk

Format Sitasi

APA MLA BibTeX

Dzadz, N., Romaniuk, M. (2024). Improving Insurance Catastrophic Data with Resampling and GAN Methods. https://arxiv.org/abs/2410.17294

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓