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International Journal of Neutrosophic Science

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Online: 2690-6805 Print: 2692-6148
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International Journal of Neutrosophic Science
Full Length Article

Volume 24Issue 2PP: 198-209 • 2024

On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times

Mohammad Abiad 1* ,
Muhammad Shafiq 2 ,
Syed Habib Shah 2 ,
Muhammad Atif 3
1College of Business Administration, American University of the Middle East, Kuwait
2Institute of Numerical Sciences, Kohat University of Science & Technology, KP, Pakistan
3Department of Statistics, University of Peshawar, KP, Pakistan
* Corresponding Author.
Received: March 11, 2024 Revised: April 07, 2024 Accepted: April 29, 2024

Abstract

Lifetime analyses comprise the techniques dealing with observations obtained from the occurrence of a specified event(s). In most of the situations dealing with lifetime observations, some units are recorded as censored observations. Dealing with censored observations makes these techniques unique. Countless standard statistical tools are available for inference based on censored lifetime observations. These classical techniques consider lifetime observations as precise numbers and ignore the uncertainty of single observations. Whereas in practical applications it is not possible to measure life times as precise numbers, they are always more or less nonprecise. The imprecision in measurements can be covered by neutrosophic set. Fuzzy estimators for life time distributions potentially use neutrosophic system to model and analyze the inherent uncertainties and neutalities present in the data and the parameter estimates. This study aimed to obtain estimators for the Weibull parameters and two exponential parameters based on the up-to-date fuzzy number approach, a special case for neutrosophic set. The suggested estimators incorporate fuzziness in addition to random variation, which makes these estimators more realistic. The same techniques need to be extended to fuzzy and neutrosophic sets.

Keywords

Characterizing function Fuzzy numbers Life time Non-precise data Neutrosophic sets

References

[1] R. Viertl, Statistical Methods for Fuzzy Data. Chichester: Wiley, 2011.

[2] R. Viertl, “On reliability estimation based on fuzzy lifetime data,” Journal of Statistical Planning and Inference, vol. 139, no. 5, pp. 1750 – 1755, 2009.

[3] J. Kalbfleisch and R. Prentice, The Statistical Analysis of Failure Time Data. New Jersey: Wiley, 2011.

[4] G. C. Stone and R. G. Van Heeswijk, “Parameter estimation for the Weibull distribution,” IEEE Transactions on Electrical Insulation, vol. E1-E12, no. 4, pp. 253–261, 1977.

[5] N. Balakrishnan and M. Kateri, “On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data,” Statistics and Probability Letters, vol. 78, no. 17, pp. 2971 – 2975, 2008.

[6] C. B. Guure and N. A. Ibrahim, “Methods for estimating the 2-parameter Weibull distribution with type I censored data,” Research Journal of Applied Sciences, Engineering and Technology, vol. 5, no. 3, pp. 689–694, 2013.

[7] H.-F. Yu and C.-Y. Peng, “Estimation for Weibull distribution with type II highly censored data,” Quality Technology and Quantitative Management, vol. 10, no. 2, pp. 193–202, 2013.

[8] B. Epstein and M. Sobel, “Life testing,” Journal of the American Statistical Association, vol. 48, pp. 486–502, 1953.

[9] B. Epstein, “Estimation of the parameters of two parameter exponential distribution from censored samples,” Technometrics, vol. 2, pp. 403–406, 1960.

[10] M. Zelen, “Applications of exponential models to problems in cancer research,” Journal of the Royal Statistical Society, Series A, vol. 129, pp. 368–398, 1966.

[11] R. Viertl and D. Hareter, Beschreibung und Analyse unscharfer Information: Statistische Methoden f¨ur unscharfe Daten. Wien: Springer, 2006.

[12] A. Pak, G. Parham, and M. Saraj, “Reliability estimation in Rayleigh distribution based on fuzzy lifetime data,” International Journal of System Assurance Engineering and Management, vol. 5, no. 4, pp. 487–494, 2013.

[13] M. Shafiq, M. Atif, and R. Viertl, “Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times,” Advances in Mechanical Engineering, vol. 9, no. 8, pp. 1–9, 2017.

[14] M. Shafiq and R. Viertl, “Bathtub hazard rate distributions and fuzzy life times,” Iranian Journal of Fuzzy Systems, vol. 14, no. 5, pp. 31–41, 2017.

[15] M. Shafiq, “Classical and bayesian inference of pareto distribution and fuzzy life times,” Pakistan Journal of Statistics, vol. 33, no. 1, pp. 15–25, 2017.

[16] M. Shafiq and R. Viertl, “On the estimation of parameters, survival functions, and hazard rates based on fuzzy life time data,” Communications in Statistics-Theory and Methods, vol. 46, no. 10, pp. 5035–5055, 2017.

[17] M. Shafiq, M. Atif, and R. Viertl, “Generalized likelihood ratio test and cox’s f-test based on fuzzy lifetime data,” International Journal of Intelligent Systems, vol. 32, no. 1, pp. 3–16, 2017.

[18] G. K. Vishwakarma, C. Paul, and N. Singh, “Parameters estimation of weibull distribution based on fuzzy data using neural network,” Biostatistics and Biometrics Open Access Journal, vol. 6, no. 5, pp. 126–133, 2018.

[19] S. Abhijit and P. Arnab, “Generalized weighted exponential similarity measures of single valued neutrosophic sets,” International Journal of Neutrosophic Science, vol. II-o, pp. 57–66, 2019.

[20] Z. Roohanizadeh, E. Baloui Jamkhaneh, and E. Deiri, “Parameters and reliability estimation for the weibull distribution based on intuitionistic fuzzy lifetime data,” Complex & Intelligent Systems, vol. 8, p. 4881–4896, 2022.

[21] S. H. Shah, M. Shafiq, and Q. Zaman, “Generalized estimation for two-parameter life time distributions based on fuzzy life times,” Mathematical Problems in Engineering, vol. 2022, 2022.

[22] N. H. Al-Noor, “Reliability estimation of lomax distribution with fuzziness,” Boletim da Sociedade Paranaense de

Matem´atica, vol. 41, pp. 1–9, 2023.

[23] A. Mazin, M and A. Zakariya, Yahya, “Neutrosophic exponentiated inverse rayleigh distribution: Properties and applications,” International Journal of Neutrosophic Science, vol. 21, no. 4, pp. 36–42, 2023.

[24] M. Marwah, Yahya and A. Zakariya, Yahya, “Neutrosophic inverse power lindley distribution: A modeling and application for bladder cancer patients,” International Journal of Neutrosophic Science, vol. 21, no. 2, pp. 216–223, 2023.

[25] M. Palanikumar, K. Nasreen, O. Emre, and O. Ebru, “Extending the concepts of complex interval valued neutrosophic subbisemiring of bisemiring,” International Journal of Neutrosophic Science, vol. 23, no. 4, pp. 117–135, 2024.

[26] Rani, P., Garg, H., & Sharma, R. (2021). "An improved TOPSIS method based on entropy and divergence measures for multi-attribute decision-making under intuitionistic fuzzy environment." Applied Soft Computing, vol. 110, pp. 107647. doi:10.1016/j.asoc.2021.107647.

[27] Gaur, A., and Jain, S. (2021). "A novel fuzzy decision-making framework using centrality measures in complex networks for optimal decision selection." Journal of Computational and Applied Mathematics, vol. 382, pp. 113014. doi:10.1016/j.cam.2020.113014.

[28] E. Ozbilge and A. Demir, “Analysis of the inverse problem in a time fractional parabolic equation with mixed boundary conditions,” Boundary Value Problems, vol. 2014, pp. 1–9, 2014.

[29] E. Ozbilge, F. Kanca, and E. O¨ zbilge, “Inverse problem for a time fractional parabolic equation with nonlocal boundary conditions,” Mathematics, vol. 10, no. 9, p. 1479, 2022.

[30] E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis. New Jersey: Wiley, 2013.

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Abiad, Mohammad, Shafiq, Muhammad, Shah, Syed Habib, Atif, Muhammad. "On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times." International Journal of Neutrosophic Science, vol. Volume 24, no. Issue 2, 2024, pp. 198-209. DOI: https://doi.org/10.54216/IJNS.240217
Abiad, M., Shafiq, M., Shah, S., Atif, M. (2024). On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times. International Journal of Neutrosophic Science, Volume 24(Issue 2), 198-209. DOI: https://doi.org/10.54216/IJNS.240217
Abiad, Mohammad, Shafiq, Muhammad, Shah, Syed Habib, Atif, Muhammad. "On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times." International Journal of Neutrosophic Science Volume 24, no. Issue 2 (2024): 198-209. DOI: https://doi.org/10.54216/IJNS.240217
Abiad, M., Shafiq, M., Shah, S., Atif, M. (2024) 'On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times', International Journal of Neutrosophic Science, Volume 24(Issue 2), pp. 198-209. DOI: https://doi.org/10.54216/IJNS.240217
Abiad M, Shafiq M, Shah S, Atif M. On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times. International Journal of Neutrosophic Science. 2024;Volume 24(Issue 2):198-209. DOI: https://doi.org/10.54216/IJNS.240217
M. Abiad, M. Shafiq, S. Shah, M. Atif, "On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times," International Journal of Neutrosophic Science, vol. Volume 24, no. Issue 2, pp. 198-209, 2024. DOI: https://doi.org/10.54216/IJNS.240217
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