Research article Special Issues

A flexible model for bounded data with bathtub shaped hazard rate function and applications

  • Received: 12 June 2024 Revised: 07 August 2024 Accepted: 09 August 2024 Published: 23 August 2024
  • MSC : 60E05, 60F05

  • The unit new X-Lindley distribution, which is a novel one-parameter distribution on the unit interval, is presented in this study. It was developed by altering the new X-Lindley distribution using the exponential transformation. This new one-parameter distribution's fundamental features, including moments, incomplete moments, Lorenz and Bonferroni curves, Gini index, mode, extropy, Havrda and Charvat entropy, Rényi entropy, and Tsallis entropy, are explored. Additionally, it has bathtub-shaped hazard rate functions and monotonically increasing hazard rate functions with a single parameter. The new distribution is therefore sufficiently rich to model real data. Also, different estimation methods, such as maximum likelihood, least-squares, and weighted least-squares, are used to estimate the parameters of this model, and using a simulation research, their respective performances are evaluated. Finally, two real-life datasets are used to demonstrate the suggested model's competency.

    Citation: M. R. Irshad, S. Aswathy, R. Maya, Amer I. Al-Omari, Ghadah Alomani. A flexible model for bounded data with bathtub shaped hazard rate function and applications[J]. AIMS Mathematics, 2024, 9(9): 24810-24831. doi: 10.3934/math.20241208

    Related Papers:

  • The unit new X-Lindley distribution, which is a novel one-parameter distribution on the unit interval, is presented in this study. It was developed by altering the new X-Lindley distribution using the exponential transformation. This new one-parameter distribution's fundamental features, including moments, incomplete moments, Lorenz and Bonferroni curves, Gini index, mode, extropy, Havrda and Charvat entropy, Rényi entropy, and Tsallis entropy, are explored. Additionally, it has bathtub-shaped hazard rate functions and monotonically increasing hazard rate functions with a single parameter. The new distribution is therefore sufficiently rich to model real data. Also, different estimation methods, such as maximum likelihood, least-squares, and weighted least-squares, are used to estimate the parameters of this model, and using a simulation research, their respective performances are evaluated. Finally, two real-life datasets are used to demonstrate the suggested model's competency.



    加载中


    [1] E. S. A. El-Sherpieny, M. A. Ahmed, On the Kumaraswamy Kumaraswamy distribution, Int. J. Basic Appl. Sci., 3 (2014), 372–381. https://doi.org/10.14419/ijbas.v3i4.3182 doi: 10.14419/ijbas.v3i4.3182
    [2] E. Altun, G. G. Hamedani, The log-xgamma distribution with inference and application, J. Soc. Fr. Stat., 159 (2018), 40–55.
    [3] J. Mazucheli, S. R. Bapat, A. F. B. Menezes, A new one-parameter unit-Lindley distribution, Chilean J. Stat., 11 (2020), 53–67.
    [4] J. Mazucheli, A. F. Menezes, S. Dey, Unit-Gompertz distribution with applications, Statistica, 79 (2019), 25–43. https://doi.org/10.6092/issn.1973-2201/8497 doi: 10.6092/issn.1973-2201/8497
    [5] J. Mazucheli, A. F. B. Menezes, L. B. Fernandes, R. P. De Oliveira, M. E. Ghitany, The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates, J. Appl. Stat., 47 (2020), 954–974. https://doi.org/10.1080/02664763.2019.1657813 doi: 10.1080/02664763.2019.1657813
    [6] K. Modi, V. Gill, Unit Burr-Ⅲ distribution with application, J. Stat. Manag. Syst., 23 (2020), 579–592. https://doi.org/10.1080/09720510.2019.1646503 doi: 10.1080/09720510.2019.1646503
    [7] R. A. R. Bantan, C. Chesneau, J. Farrukh, M. Elgarhy, M. H. Tahir, A. Ali, et al., Some new facts about the unit-Rayleigh distribution with applications, Mathematics, 8 (2020), 1954. https://doi.org/10.3390/math8111954 doi: 10.3390/math8111954
    [8] M. Ç. Korkmaz, C. Chesneau, On the unit Burr-XII distribution with the quantile regression modeling and applications, Comput. Appl. Math., 40 (2021), 29. https://doi.org/10.1007/s40314-021-01418-5 doi: 10.1007/s40314-021-01418-5
    [9] H. S. Bakouch, A. S. Nik, A. Asgharzadeh, H. S. Salinas, A flexible probability model for proportion data: Unit-half-normal distribution, Commun. Stat. Case Stud. Data Anal. Appl., 7 (2021), 271–288. https://doi.org/10.1080/23737484.2021.1882355 doi: 10.1080/23737484.2021.1882355
    [10] M. Irshad, V. Dcruz, R. Maya, The exponentiated unit Lindley distribution: Properties and applications, Ricerche Mat., 73 (2021), 1121–1143. https://doi.org/10.1007/s11587-021-00663-4 doi: 10.1007/s11587-021-00663-4
    [11] A. Krishna, R. Maya, C. Chesneau, M. R. Irshad, The unit Teissier distribution and its applications, Math. Comput. Appl., 27 (2022), 12. http://dx.doi.org/10.3390/mca27010012 doi: 10.3390/mca27010012
    [12] A. I. Al-Omari, A. R. Alanzi, S. S. Alshqaq, The unit two parameters Mirra distribution: Reliability analysis, properties, estimation and applications, Alex. Eng. J., 92 (2024), 238–253. https://doi.org/10.1016/j.aej.2024.02.063 doi: 10.1016/j.aej.2024.02.063
    [13] R. Maya, P. Jodra, M. Irshad, A. Krishna, The unit Muth distribution: statistical properties and applications, Ricerche Mat., 73 (2022), 1843–1866. https://doi.org/10.1007/s11587-022-00703-7 doi: 10.1007/s11587-022-00703-7
    [14] N. Khodja, A. M. Gemeay, H. Zeghdoudi, K. Karakaya, A. M. Alshangiti, M. E. Bakr, et al., Modeling voltage real data set by a new version of Lindley distribution, IEEE Access, 11 (2023), 67220–67229. https://doi.org/10.1109/ACCESS.2023.3287926 doi: 10.1109/ACCESS.2023.3287926
    [15] A. Beghriche, H. Zeghdoudi, V. Raman, S. Chouia, New polynomial exponential distribution: Properties and applications, Statist. Transit., 23 (2022), 95–112. https://doi.org/10.2478/stattrans-2022-0032 doi: 10.2478/stattrans-2022-0032
    [16] A. A. Abd EL-Baset, M. Ghazal, Exponentiated additive Weibull distribution, Reliab. Eng. Syst. Saf., 193 (2020), 106663. http://dx.doi.org/10.1016/j.ress.2019.106663 doi: 10.1016/j.ress.2019.106663
    [17] M. Irshad, D. Shibu, R. Maya, V. Dcruz, Binominal mixture Lindley distribution: Properties and applications, J. Indian Soc. Probab. Stat., 21 (2020), 437–469. https://doi.org/10.1007/s41096-020-00090-y doi: 10.1007/s41096-020-00090-y
    [18] A. Khalil, M. Ijaz, K. Ali, W. K. Mashwani, M. Shafiq, P. Kumam, et al., A novel flexible additive Weibull distribution with real-life applications, Comm. Statist. Theory Methods, 50 (2021), 1557–1572. https://doi.org/10.1080/03610926.2020.1732658 doi: 10.1080/03610926.2020.1732658
    [19] M. E. Ghitany, J. Mazucheli, A. F. B. Menezes, F. Alqallaf, The unit-inverse Gaussian distribution: A new alternative to two-parameter distributions on the unit interval, Commun. Stat. Theory Methods, 48 (2019), 3423–3438. https://doi.org/10.1080/03610926.2018.1476717 doi: 10.1080/03610926.2018.1476717
    [20] M. Ç. Korkmaz, A new heavy-tailed distribution defined on the bounded interval: The logit Slash distribution and its application, J. Appl. Stat., 47 (2020), 2097–2119. https://doi.org/10.1080/02664763.2019.1704701 doi: 10.1080/02664763.2019.1704701
    [21] Y. A. Iriarte, M. de Castro, H. W. Gómez, An alternative one-parameter distribution for bounded data modeling generated from the Lambert transformation, Symmetry, 13 (2021), 1190. http://dx.doi.org/10.3390/sym13071190 doi: 10.3390/sym13071190
    [22] J. Mazucheli, M. Ç. Korkmaz, A. F. B. Menezes, V. Leiva, The unit generalized half-normal quantile regression model: Formulation, estimation, diagnostics, and numerical applications, Soft Comput., 27 (2023), 279–295. http://dx.doi.org/10.1007/s00500-022-07278-3 doi: 10.1007/s00500-022-07278-3
    [23] F. Lad, G. Sanfilippo, G. Agro, Extropy: Complementary dual of entropy, Statist. Sci., 30 (2015), 40–58. http://doi.org/10.1214/14-STS430 doi: 10.1214/14-STS430
    [24] C. Tsallis, Possible generalization of Boltzmann-Gibbs statistics, J. Stat. Phys., 52 (1988), 479–487. http://dx.doi.org/10.1007/BF01016429 doi: 10.1007/BF01016429
    [25] A. Rényi, On measures of entropy and information, Berkeley Symp. Math. Statist. Prob., 4.1 (1961), 547–562.
    [26] J. Havrda, F. Charvat, Quantification method of classification processes, concept of structural $\alpha$-entropy, Kybernetika, 3 (1967), 30–35.
    [27] A. Al-Shomrani, O. Arif, A. Shawky, S. Hanif, M. Q. Shahbaz, Topp-Leone family of distributions: Some properties and application, Pak. J. Stat. Oper. Res., 12 (2016), 443–451. http://dx.doi.org/10.18187/pjsor.v12i3.1458 doi: 10.18187/pjsor.v12i3.1458
    [28] P. Kumaraswamy, A generalized probability density function for double-bounded random processes, J. Hydrol., 46 (1980), 79–88. https://doi.org/10.1016/0022-1694(80)90036-0 doi: 10.1016/0022-1694(80)90036-0
    [29] A. Pourdarvish, S. M. T. K. Mirmostafaee, K. Naderi, The exponentiated Topp-Leone distribution: Properties and application, J. Appl. Environ. Biol. Sci., 5 (2015), 251–256.
    [30] M. Caramanis, J. Stremel, W. Fleck, S. Daniel, Probabilistic production costing: An investigation of alternative algorithms, Int. J. Elec. Power Energy Syst., 5 (1983), 75–86. https://doi.org/10.1016/0142-0615(83)90011-X doi: 10.1016/0142-0615(83)90011-X
    [31] M. Mazumdar, D. P. Gaver, On the computation of power-generating system reliability indexes, Technometrics, 26 (1984), 173–185. https://doi.org/10.1080/00401706.1984.10487942 doi: 10.1080/00401706.1984.10487942
    [32] P. Sudsila, A. Thongteeraparp, S. Aryuyuen, W. Bodhisuwan, The generalized distributions on the unit interval based on the T-Topp-Leone family of distributions, Trends Sci., 19 (2022), 6186–6186. https://doi.org/10.48048/tis.2022.6186
    [33] M. V. Aarset, How to identify bathtub hazard rate, IEEE Trans. Reliab., 36 (1987), 106–108. https://doi.org/10.1109/TR.1987.5222310 doi: 10.1109/TR.1987.5222310
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(466) PDF downloads(50) Cited by(0)

Article outline

Figures and Tables

Figures(9)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog