A Hybrid Support Vector Machine–Genetic Algorithm Framework Approach for Parameters Estimation for the [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh Process in nonhomogenous Poisson Process

Authors

  • Ahmed Yousif Department of Mathematics and Computer Applications, College of Applied Sciences, University of Technology, Baghdad, Iraq
  • Haifa Ali Department of Administrative and Financial Affairs, Faculty Division, Al-Iraqi University, Baghdad, Iraq
  • Hasanain Alsaedi Business Information Technology Department, College of Business Information, University Information Technology and Communication, Baghdad, Iraq
  • Mohammad Tashtoush Depatrment of Basic Sciences, AL-Huson University College, AL-Balqa Applied University, Salt 19117, Jordan; Faculty of Education and Arts, Sohar University, Sohar 311, Oman
  • Adel Hussain IT Department, Amedi Technical Institutes, University of Duhok Polytechnic, Duhok, Iraq; Administrative & Financial Affairs Department, Al-Iraqia University, Baghdad, Iraq; Department of Computer Engineering, Al-Kitab University, Altun, Kupri, Iraq

DOI:

https://doi.org/10.19139/soic-2310-5070-3378

Keywords:

Truncated, Survival estimation, Inverse Rayleigh Process, Newton Raphson, SVM-GA, nonhomogeneous Poisson Process, Simulation

Abstract

The estimation of three unknown parameters of [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh Process of nonhomogeneous Poisson Process (TNHIRP). It discusses nine of those estimation methods, which are the maximum likelihood, maximum product spacing, Anderson Darling, Right Anderson-Darling, ordinary least squares, weighted least squares, Cramer-von Mises, Hybrid Support Vector Machine–Genetic Algorithm, and percentiles to estimate points. These estimators were compared in terms of bias and mean squared errors using detailed analysis and a lot of simulation experiments. According to the simulation outcomes, all the estimators work well under these considerations and show similar values in case of large sample size. There are also some statistical properties of the new distribution derived. The estimators were also applied to two real life datasets and the values of the Kolmogorov-Smirnoff statistics reported. Lastly, the [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh process was tested on two real world datasets, and the performance was compared with other well-known extensions of Inverse Rayleigh process using four selection criteria.

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Published

2026-03-05

How to Cite

Yousif, A., Ali, H., Alsaedi, H., Tashtoush, M., & Hussain, A. (2026). A Hybrid Support Vector Machine–Genetic Algorithm Framework Approach for Parameters Estimation for the [0,1] Truncated Nadarajah Haghighi Inverse Rayleigh Process in nonhomogenous Poisson Process . Statistics, Optimization & Information Computing, 15(6), 4805–4826. https://doi.org/10.19139/soic-2310-5070-3378

Issue

Vol. 15 No. 6 (2026)

Section

Research Articles

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