Reliability Analysis with New Sine Inverse Rayleigh Distribution

Authors

  • Ikwuoche John David Federal University Wukari, Nigeria
  • Mathew Stephen Kwararafa University Wukari, Nigeria
  • Eghwerido Joseph Thomas Federal University of Petroleum Resources, Effurun, Nigeria

DOI:

https://doi.org/10.13052/jrss0974-8024.1623

Keywords:

Well-behaved, New Sine G, MLE, reliability

Abstract

This article examined some of the characteristics of the New Sine Inverse Rayleigh Distribution. There is just one scale parameter in the New Sine Inverse Rayleigh distribution. The raw moments, reliability analysis, and other aspects of the New Sine Inverse Rayleigh Distribution have been derived. The maximum likelihood approach was used to estimate the New Sine Inverse Rayleigh Distribution’s parameters. Utilizing simulation, the distribution’s stability was examined, and the applicability of the distribution was demonstrated using three data sets. The analysis’s findings demonstrated that the New Sine Inverse Rayleigh Distribution behaves well and fits the data more closely than other probability distributions.

Downloads

Download data is not yet available.

Author Biographies

Ikwuoche John David, Federal University Wukari, Nigeria

Ikwuoche John David obtained his B.Sc. and M.Sc. in 2009 and 2012 respectively, from the Department of Mathematics, Ahmadu Bello University, Zaria-Nigeria. In 2021 he obtained his Ph.D. in Statistics from the Department of Statistics, Ahmadu Bello University, Zaria-Nigeria. Currently he is a Senior Lecturer and Departmental Postgraduate Co-ordinator for Mathematics and Statistics programs in the Department of Mathematics and Statistics, Federal University Wukari, Nigeria.

 

 

Mathew Stephen, Kwararafa University Wukari, Nigeria

Mathew Stephen received his Diploma and B.Sc. in Statistics from The Federal Polytechnic Mubi, Adamawa State and Federal University Wukari, Wukari Taraba State, Nigeria in years 2014 and 2018 respectively. Presently, he pursues an MSc in statistics at Federal University Wukari, Wukari Taraba State Nigeria. He is also a Graduate Assistant at Kwararafa University Wukari, Wukari Taraba State Nigeria.

Eghwerido Joseph Thomas, Federal University of Petroleum Resources, Effurun, Nigeria

Eghwerido Joseph Thomas received his B.Sc. and M.Sc. in Statistics from the Obafemi Awolowo University Ile-Ife in years 2008 and 2013 respectively. He then proceeded to the University of Benin Edo State, Nigeria where he obtained his Ph.D. in Industrial Mathematics with Options in Statistics in 2019. He is the Pioneer and current Head of the Department of Statistics, Federal University of Petroleum Resources, Effurun, Delta State, Nigeria. He has several publications in Spatial Statistics, Data Science, Portfolio Management, Probability Theory, and Distribution Theory. He is a member of the Editorial Team of Mathematical Reviews (MathSciNet), Mathematica Slovaca, Scientific African, Thailand Statistician, Fupre Journal of Scientific and Industrial Research, Gazi University Journal of Science, Scientometrics, Austrian Journal of Statistics, and lots more. He is a member of the Nigerian Statistical Association, Nigerian Mathematical Society, and International Biometric Society.

 

References

Al-Anber N.J. (2020). Lomax-Rayleigh distribution: traditional and heuristic methods of estimation, in: Journal of Physics: Conference Series, vol. 1591, IOP Publishing, 1–12

Smith, R. L., and Naylor, J. (1987). A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution. Journal of the Royal Statistical Society Series C: Applied Statistics, 36(3), 358–369.

Bader, M. G., and Priest, A. M. (1982). Statistical aspects of fibre and bundle strength in hybrid composites. Progress in science and engineering of composites, 1129–1136.

Gross, A. J., and Clark, V. (1975). Survival distributions: reliability applications in the biomedical sciences. (No Title).

Al-Noor N. and Assi N., (2020). Rayleigh-Rayleigh distribution: properties and applications, in: Journal of Physics: Conference Series, vol. 1591, IOP Publishing. 1–15.

Greene M. J., (2019). The Epsilon-Skew Rayleigh Distribution. A Dissertation Submitted to the Graduate School University of Arkansas at Little Rock.

Ieren T.G., Abdulkadir S.S. and Issa A.A., (2020). Odd Lindley-Rayleigh distribution: its properties and applications to simulated and real life datasets, J. Adv. Math. Comput. Sci. 35(1) 63–88.

Kundu D. and Raqab M.Z., (2005). Generalized Rayleigh distribution: different methods of estimations, Comput. Stat. Data Anal. 49(1) 187–200.

Mahmood, Z. Chesneau, C. and Tahir, M.H. (2019). A new sine-G family of distributions: Properties and applications. Bull. Comput. Appl. Math., 7, 53–81.

Malik, A. S., and Ahmad, S. P. (2019). Transmuted Alpha Power Inverse Rayleigh Distribution: Properties and Application. Journal of Scientific Research, 11(2).

Merovci F., and Elbatal I., (2015). Weibull-Rayleigh distribution: theory and applications, Appl. Math. Inf. Sci. 9(5) 1–11.

Merovci F., (2013). Transmuted Rayleigh distribution, Aust. J. Stat. 42(1) 21–31.

Voda V.G., (2007). A new generalization of Rayleigh distribution, Reliab. Theory Appl. 2(6) 47–56.

Downloads

Published

2023-12-26

How to Cite

David, I. J. ., Stephen, M. ., & Thomas, E. J. . (2023). Reliability Analysis with New Sine Inverse Rayleigh Distribution. Journal of Reliability and Statistical Studies, 16(02), 255–268. https://doi.org/10.13052/jrss0974-8024.1623

Issue

2023: Vol 16 Iss 2

Section

Articles