Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data

Chrisandi Rantegau Lande; Nur Iriawan; Dedy Dwi Prastyo

doi:10.1016/j.procs.2024.10.275

Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data

Chrisandi Rantegau Lande
, Nur Iriawan^*
, Dedy Dwi Prastyo

^*Corresponding author for this work

Department of Statistics
Institut Teknologi Sepuluh Nopember
Politeknik Ilmu Pelayaran Makassar

Research output: Contribution to journal › Conference article › peer-review

Abstract

This article analyzes the progression of the adjusted signal-to-noise ratio (SNR) data for the GEVMAR model. The practical and consistent simulations at any given sample size were achieved by constructing and estimating the GEVMAR model and utilizing the adjusted SNR measure. In addition, upon comparing the adjusted signal-to-noise ratio (SNR) values for the GEVMAR model with the claim reserves data, it is evident that the GEVMAR model exhibits a 2% higher adjusted SNR value than the GMAR model.

Original language	English
Pages (from-to)	490-499
Number of pages	10
Journal	Procedia Computer Science
Volume	245
DOIs	https://doi.org/10.1016/j.procs.2024.10.275
Publication status	Published - 2024
Event	9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024 - Bali, Indonesia Duration: 6 Sept 2023 → 8 Sept 2023

Keywords

GEVMAR
adjusted SNR
claim reserves
time series

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10.1016/j.procs.2024.10.275

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@article{95d3ac09589a49c98c47da0f0167c175,

title = "Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data",

abstract = "This article analyzes the progression of the adjusted signal-to-noise ratio (SNR) data for the GEVMAR model. The practical and consistent simulations at any given sample size were achieved by constructing and estimating the GEVMAR model and utilizing the adjusted SNR measure. In addition, upon comparing the adjusted signal-to-noise ratio (SNR) values for the GEVMAR model with the claim reserves data, it is evident that the GEVMAR model exhibits a 2\% higher adjusted SNR value than the GMAR model.",

keywords = "GEVMAR, adjusted SNR, claim reserves, time series",

author = "Lande, \{Chrisandi Rantegau\} and Nur Iriawan and Prastyo, \{Dedy Dwi\}",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024 ; Conference date: 06-09-2023 Through 08-09-2023",

year = "2024",

doi = "10.1016/j.procs.2024.10.275",

language = "English",

volume = "245",

pages = "490--499",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data

AU - Lande, Chrisandi Rantegau

AU - Iriawan, Nur

AU - Prastyo, Dedy Dwi

PY - 2024

Y1 - 2024

N2 - This article analyzes the progression of the adjusted signal-to-noise ratio (SNR) data for the GEVMAR model. The practical and consistent simulations at any given sample size were achieved by constructing and estimating the GEVMAR model and utilizing the adjusted SNR measure. In addition, upon comparing the adjusted signal-to-noise ratio (SNR) values for the GEVMAR model with the claim reserves data, it is evident that the GEVMAR model exhibits a 2% higher adjusted SNR value than the GMAR model.

AB - This article analyzes the progression of the adjusted signal-to-noise ratio (SNR) data for the GEVMAR model. The practical and consistent simulations at any given sample size were achieved by constructing and estimating the GEVMAR model and utilizing the adjusted SNR measure. In addition, upon comparing the adjusted signal-to-noise ratio (SNR) values for the GEVMAR model with the claim reserves data, it is evident that the GEVMAR model exhibits a 2% higher adjusted SNR value than the GMAR model.

KW - GEVMAR

KW - adjusted SNR

KW - claim reserves

KW - time series

UR - https://www.scopus.com/pages/publications/85213025153

U2 - 10.1016/j.procs.2024.10.275

DO - 10.1016/j.procs.2024.10.275

M3 - Conference article

AN - SCOPUS:85213025153

SN - 1877-0509

VL - 245

SP - 490

EP - 499

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

Y2 - 6 September 2023 through 8 September 2023

ER -

Adjusted SNR for Generalized Extreme Value Mixture Autoregressive Model in Actuarial Data

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Keywords

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