A novel extension of half-logistic distribution with statistical inference, estimation and applications.
Estimation methods
Half logistic distribution
Monte Carlo simulation
Odd Frechet-G family
Quantile function
Statistical properties
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 Feb 2024
21 Feb 2024
Historique:
received:
27
07
2023
accepted:
05
02
2024
medline:
22
2
2024
pubmed:
22
2
2024
entrez:
21
2
2024
Statut:
epublish
Résumé
In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model's capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.
Identifiants
pubmed: 38383570
doi: 10.1038/s41598-024-53768-9
pii: 10.1038/s41598-024-53768-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4326Informations de copyright
© 2024. The Author(s).
Références
Balakrishnan, N. Order statistics from the half logistic distribution. J. Stat. Comput. Simul. 20(4), 287–309 (1985).
doi: 10.1080/00949658508810784
Balakrishnan, N. & Puthenpura, S. Best linear unbiased estimators of location and scale parameters of the half logistic distribution. J. Stat. Comput. Simul. 25(3–4), 193–204 (1986).
doi: 10.1080/00949658608810932
Balakrishnan, N. & Wong, K. H. T. Approximate MLEs for the location & scale parameters of the half-logistic distribution with type-I1 right-censoring. IEEE Trans. Reliab 40(2), 140–145 (1991).
doi: 10.1109/24.87114
Olapade, A. K. On characterizations of the half-logistic distribution. Interstat 2, (2003).
Rosaiah, K., Kantam, R. R. L. & Rao, B. S. Reliability test plan for half-logistic distribution. Calcutta Stat. Assoc. Bull. 61, 241–244 (2009).
Torabi, H. & Bagheri, F. L. Estimation of parameters for an extended generalized half-logistic distribution based on complete and censored data. J. Iran. Stat. Soc. 9(2), 171–195 (2010).
Rao, B. S., Nagendram, S. & Rosaiah, K. Exponential half-logistic additive failure rate model. Int. J. Sci. Res. Publ. 3(5), 1–10 (2013).
Olapade, A. K. The type I generalized half-logistic distribution. J. Iran. Stat. Soc. 13(1), 69–82 (2014).
Cordeiro, G. M., Alizadeh, M. & Ortega, E. M. M. The exponentiated half-logistic family of distributions: Properties and applications. J. Probab. Stat. 2014.
Krishnarani, S. D. On a power transformation of half-logistic distribution. J. Probab. Stat. 2016.
Yegen, D. & Özel, G. Marshall-Olkin half-logistic distribution with theory and applications. Alphanumer. J. 6(2), 407–416 (2018).
doi: 10.17093/alphanumeric.409992
Bourguignon, M., Silva, R. B. & Cordeiro, G. M. The Weibull-G family of probability distributions. J. Data Sci. 12, 53–68 (2014).
doi: 10.6339/JDS.201401_12(1).0004
Hassana, A. S., Shawkia, A. W. & Muhammeda, H. Z. Weighted Weibull-G family of distributions: Theory & application in the analysis of renewable energy sources. J. Posit. School Psychol. 6(3), 9201–9216 (2022).
Cordeiro, G. M. & de Castro, M. A new family of generalized distributions. J. Stat. Comput. Simul. 81(7), 883–898 (2011).
doi: 10.1080/00949650903530745
Lone, M. A., Dar, I. H. & Jan, T. R. A new method for generating distributions with an application to Weibull distribution. Reliab. Theory Appl. 17(1), 223–239 (2022).
Lone, M. A., Dar, I. H. & Jan, T. R. An innovative method for generating distributions: Applied to Weibull distribution. J. Sci. Res. 66(3), 308–315 (2022).
Haq, M. A. U. & Elgarhy, M. The odd Frechet-G family of probability distributions. J. Stat. Appl. Probab. 7(1), 189–203 (2018).
doi: 10.18576/jsap/070117
ZeinEldin, R. A., Ahsan Ul Haq, M., Hashmi, S., Elsehety, M. & Elgarhy, M. Statistical inference of odd Fréchet inverse lomax distribution with applications. Complexity 2020, 1–20 (2020).
Elgarhy, M. & Alrajhi, S. The odd Fréchet inverse rayleigh distribution: Statistical properties and applications. J. Nonlinear Sci. Appl. 12(05), 291–299 (2018).
doi: 10.22436/jnsa.012.05.03
Alrajhi, S. The odd Fréchet inverse exponential distribution with application. J. Nonlinear Sci. Appl. 12(08), 535–542 (2019).
doi: 10.22436/jnsa.012.08.04
Fayomi, A. The odd Frechet inverse Weibull distribution with application. J. Nonlinear Sci. Appl. 12(03), 165–172 (2018).
doi: 10.22436/jnsa.012.03.04
Ahsan ul Haq, M., Albassam, M., Aslam, M. & Hashmi, S. Statistical inferences on odd Fréchet power function distribution. J. Reliab. Stat. Stud. 14(1), 141–172 (2021).
Anderson, T. W. & Darling, D. A. A test of goodness of fit. J. Am. Stat. Assoc. 49(268), 765–769 (1954).
doi: 10.1080/01621459.1954.10501232
Macdonald, P. D. M. Comment on “An estimation procedure for mixtures of distributions” by Choi and Bulgren. J. R. Stat. Soc. B Stat. Methodol. 33(2), 326–329 (1971).
Cheng, R. C. H. & Amin, N. A. K. Estimating parameters in continuous univariate distributions with a shifted origin. J. R. Stat. Soc. B Stat. Methodol. 45(3), 394–403 (1983).
Swain, J. J., Venkatraman, S. & Wilson, J. R. Least-squares estimation of distribution functions in johnson’s translation system. J. Stat. Comput. Simul. 29(4), 271–297 (1988).
doi: 10.1080/00949658808811068
Almongy, H. M., Almetwally, E. M., Aljohani, H. M., Alghamdi, A. S. & Hafez, E. H. A new extended rayleigh distribution with applications of COVID-19 data. Results Phys. 23, 1–9 (2021).
doi: 10.1016/j.rinp.2021.104012
Bekker, A., Roux, J. J. J. & Mostert, P. J. A generalization of the compound Rayleigh distribution: Using a Bayesian method on cancer survival times. Commun. Stat. Theory Methods 29(7), 1419–1433 (2000).
doi: 10.1080/03610920008832554
Fulment, A. K., Gadde, S. R. & Peter, J. K. The odd log-logistic generalized exponential distribution: Application on survival times of chemotherapy patients data. F1000research 11, 1444 (2023).
doi: 10.12688/f1000research.127363.2
Aarset, M. V. How to identify a bathtub hazard rate. IEEE Trans. Reliab. 36(1), 106–108 (1987).
doi: 10.1109/TR.1987.5222310
Iftikhar, A., Ali, A. & Hanif, M. Half circular modified burr−III distribution application with different estimation methods. Plos ONE 17(5), e0261901 (2022).
doi: 10.1371/journal.pone.0261901
pubmed: 35580119
pmcid: 9113593