TY - JOUR
T1 - Estimation of Parameters for a New Model
T2 - Real Data Application and Simulation
AU - Hussam, Eslam
AU - Habadi, Maryam Ibrahim
AU - Albayyat, Ramlah H.
AU - Mohammed, Mohammed Omar Musa
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/5
Y1 - 2025/5
N2 - Effective analysis of survival and renewable energy data is essential to understand complex engineering phenomena. Probability distribution models offer a structured approach to uncovering patterns in such data, particularly for studying disease progression, survival analysis, and many more. In this study, we explore a novel probability distribution using the Harris extended transformation based on the Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, the maximum likelihood estimation method is evaluated, and its effectiveness is assessed through a detailed simulation study to confirm the reliability and consistency of its parameters. The practical applicability of the developed model is demonstrated with an analysis of engineering and energy data sets, comparing its performance with several well-known distributions. The results highlight the flexibility and precision of the model, establishing it as a powerful and reliable tool for advanced statistical analysis in survival and engineering research.
AB - Effective analysis of survival and renewable energy data is essential to understand complex engineering phenomena. Probability distribution models offer a structured approach to uncovering patterns in such data, particularly for studying disease progression, survival analysis, and many more. In this study, we explore a novel probability distribution using the Harris extended transformation based on the Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, the maximum likelihood estimation method is evaluated, and its effectiveness is assessed through a detailed simulation study to confirm the reliability and consistency of its parameters. The practical applicability of the developed model is demonstrated with an analysis of engineering and energy data sets, comparing its performance with several well-known distributions. The results highlight the flexibility and precision of the model, establishing it as a powerful and reliable tool for advanced statistical analysis in survival and engineering research.
KW - Harris extended transformation
KW - MLE procedure
KW - Probability distribution
KW - Renewable energy datasets
KW - Simulation study
UR - http://www.scopus.com/inward/record.url?scp=105000349781&partnerID=8YFLogxK
U2 - 10.1016/j.aej.2025.02.112
DO - 10.1016/j.aej.2025.02.112
M3 - Article
AN - SCOPUS:105000349781
SN - 1110-0168
VL - 122
SP - 543
EP - 554
JO - Alexandria Engineering Journal
JF - Alexandria Engineering Journal
ER -
