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Peer-reviewedCC BY 4.0Axioms

The Cauchy–Modified Garima Distribution: Applications to Device Lifetimes and Aircraft Windshield Failures

  • Ayşe Metin Karakaş1
  • Fatma Bulut2
  • Murat Karakaş2
  1. 1Department of Statistics, Faculty of Arts and Sciences, Bitlis Eren University, Bitlis 13000, Turkey
  2. 2Department of Mathematics, Faculty of Arts and Sciences, Bitlis Eren University, Bitlis 13000, Turkey
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Abstract

This study introduces the Cauchy–Modified Garima (CMG) distribution, a flexible two-parameter lifetime model obtained by applying a Cauchy-type generator to the Modified Garima distribution. Several statistical properties of the model are derived, and parameter estimation is investigated using maximum likelihood and Bayesian methods. Monte Carlo simulations show that both estimation procedures become more accurate as the sample size increases. Applications to real lifetime data demonstrate that the CMG distribution provides a competitive and flexible alternative to several classical lifetime models.

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Cite

BibTeXRIS
BibTeX
@article{karakas2026cauchymodified,
  title = {{The Cauchy–Modified Garima Distribution: Applications to Device Lifetimes and Aircraft Windshield Failures}},
  author = {Ayşe Metin Karakaş and Fatma Bulut and Murat Karakaş},
  journal = {Axioms},
  year = {2026},
  month = oct,
  doi = {10.3390/axioms15100746},
  url = {https://www.mdpi.com/2075-1680/15/10/746},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - The Cauchy–Modified Garima Distribution: Applications to Device Lifetimes and Aircraft Windshield Failures
AU  - Ayşe Metin Karakaş
AU  - Fatma Bulut
AU  - Murat Karakaş
PY  - 2026
DA  - 2026/10/06
JO  - Axioms
DO  - 10.3390/axioms15100746
UR  - https://www.mdpi.com/2075-1680/15/10/746
AB  - This study introduces the Cauchy–Modified Garima (CMG) distribution, a flexible two-parameter lifetime model obtained by applying a Cauchy-type generator to the Modified Garima distribution. Several statistical properties of the model are derived, and parameter estimation is investigated using maximum likelihood and Bayesian methods. Monte Carlo simulations show that both estimation procedures become more accurate as the sample size increases. Applications to real lifetime data demonstrate that the CMG distribution provides a competitive and flexible alternative to several classical lifetime models.
SN  - 2075-1680
ER  -