Research Article
BibTex RIS Cite

On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values

Year 2025, Volume: 54 Issue: 1, 291 - 318, 28.02.2025
https://doi.org/10.15672/hujms.1489598

Abstract

In this article, we consider the estimation of the stress-strength reliability parameter for the inverse Lindley distribution based on lower record values. The maximum likelihood estimator and its asymptotic distribution are obtained. An approximate classical confidence interval, as well as two bootstrap-type confidence intervals for the reliability parameter are derived. The Bayesian inference for the parameter has been considered using Tierney and Kadane’s approximation method, as well as two Monte Carlo methods, namely the Metropolis-Hastings and importance sampling techniques under both symmetric and asymmetric loss functions. Besides, the Chen and Shao shortest width credible intervals are constructed for the stress-strength parameter. A simulation study and a real data example are conducted to explore and compare the performances of the presented results.

References

  • [1] G.A. Abd-Elmougod and H.H. Abu-Zinadah, Estimation of $P (Y<X)$ for twoparameter bathtub shape distribution using records: Bayesian and non-Bayesian approaches, J. Comput. Theor. Nanosci. 14 (2), 1127-1135, 2017.
  • [2] J. Albert, Bayesian Computation with R, 2nd Edition, Dordrecht: Springer, 2009.
  • [3] R. Alotaibi, M. Nassar and A. Elshahhat, Statistical analysis of inverse Lindley data using adaptive Type-II progressively hybrid censoring with applications, Axioms 12 (5), 427, 2023.
  • [4] B.C. Arnold, N. Balakrishnan and H.N. Nagaraja, Records, New York: John Wiley & Sons, 1998.
  • [5] A. Asgharzadeh, M. Alizadeh and M.Z. Raqab, Inverse Lindley distribution: different methods for estimating their PDF and CDF, J. Stat. Comput. Simul. 94 (3), 604-623, 2024.
  • [6] A. Asgharzadeh, R. Valiollahi and M.Z. Raqab, Estimation of $Pr (Y<X)$ for the two-parameter generalized exponential records, Comm. Statist. Simulation Comput. 46 (1), 379-394, 2017.
  • [7] A. Baklizi, Estimation of $Pr (X<Y)$ using record values in the one and two parameter exponential distributions, Comm. Statist. Theory Methods 37 (5), 692-698, 2008.
  • [8] A. Baklizi, Likelihood and Bayesian estimation of $Pr (X<Y)$ using lower record values from the generalized exponential distribution, Comput. Statist. Data Anal. 52 (7), 3468-3473, 2008.
  • [9] A. Baklizi, Bayesian inference for $Pr (Y<X)$ in the exponential distribution based on records, Appl. Math. Model. 38 (5-6), 1698-1709, 2014.
  • [10] A. Baklizi, Interval estimation of the stress-strength reliability in the two-parameter exponential distribution based on records, J. Stat. Comput. Simul. 84 (12), 2670-2679, 2014.
  • [11] S. Basu, S.K. Singh and U. Singh, Parameter estimation of inverse Lindley distribution for Type-I censored data, Comput. Statist. 32 (1), 367-385, 2017.
  • [12] S. Basu, S.K. Singh and U. Singh, Estimation of inverse Lindley distribution using product of spacings function for hybrid censored data, Methodol. Comput. Appl. Probab. 21 (4), 1377-1394, 2019.
  • [13] S. Bennett, Log-logistic regression models for survival data, J. Roy. Statist. Soc. Ser. C 32 (2), 165-171, 1983.
  • [14] R. Calabria and G. Pulcini, An engineering approach to Bayes estimation for the Weibull distribution, Microelectron. Reliab. 34 (5), 789-802, 1994.
  • [15] R. Calabria and G. Pulcini, Point estimation under asymmetric loss functions for left-truncated exponential samples, Comm. Statist. Theory Methods 25 (3), 585-600, 1996.
  • [16] R. Castro, Lectures 2 and 3 - Goodness-of-fit (GoF) Tests, 2013, https://rmcastro. win.tue.nl/AppStat2013/files/lectures23.pdf.
  • [17] A. Chaturvedi, S.K. Singh and U. Singh, Maximum product spacings estimator for fuzzy data using inverse Lindley distribution, Austrian J. Stat. 52 (2), 86-103, 2023.
  • [18] M.H. Chen and Q.M. Shao, Monte Carlo estimation of Bayesian credible and HPD intervals, J. Comput. Graph. Statist. 8 (1), 69-92, 1999.
  • [19] J.Y. Chiang, N. Jiang, T.R. Tsai and Y.L. Lio, Inference of $\delta= P (X< Y)$ for Burr XII distributions with record samples, Comm. Statist. Simulation Comput. 47 (3), 822-838, 2018.
  • [20] J.D. Church and B. Harris, The estimation of reliability from stress-strength relationships, Technometrics 12 (1), 49-54, 1970.
  • [21] F. Çiftci, B. Saraçoglu, N. Akdam and Y. Akdogan, Estimation of stress-strength reliability for generalized Gompertz distribution under progressive type-II censoring, Hacet. J. Math. Stat. 52 (5), 1379-1395, 2023.
  • [22] M.J. Crowder, Tests for a family of survival models based on extremes, in: Recent Advances in Reliability Theory: Methodology, Practice, and Inference, Eds. N. Limnios and M. Nikulin, Birkhäuser, Boston, 307-321, 2000.
  • [23] A.C. Davison and D.V. Hinkley, Bootstrap Methods and Their Application, Cambridge: Cambridge University Press, 1997.
  • [24] S. Dey, M. Nassar, D. Kumar, A. Alzaatreh and M.H. Tahir, A new lifetime distribution with decreasing and upside-down bathtub-shaped hazard rate function, Statistica 79 (4), 399-426, 2019.
  • [25] S. Dey and M. Saha, Bootstrap confidence intervals of the difference between two generalized process capability indices for inverse Lindley distribution, Life Cycle Reliab. Saf. Eng. 7 (2), 89-96, 2018.
  • [26] B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans, CBMS-NSF Regional Conference Series in Applied Mathematics 38, Philadelphia: Society for Industrial and Applied Mathematics (SIAM), 1982.
  • [27] B. Etemad Golestani, E. Ormoz and S.M.T.K. MirMostafaee, Statistical inference for the inverse Lindley distribution based on lower record values, REVSTAT, 2024, https://revstat.ine.pt/index.php/REVSTAT/article/view/559.
  • [28] M.K. Hassan, M.I. Alohali and F.A. Alojail, A new application of $R= P [Y< X]$ for the inverse Lindley distribution using ranked set sampling, J. Stat. Manag. Syst. 24 (8), 1713-1731, 2021.
  • [29] W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1), 97-109, 1970.
  • [30] S. Joo and J. Mi, Some properties of hazard rate functions of systems with two components, J. Statist. Plann. Inference 140 (2), 444-453, 2010.
  • [31] A. Joukar, M. Ramezani and S.M.T.K. MirMostafaee, Estimation of $P (X> Y)$ for the power Lindley distribution based on progressively type II right censored samples, J. Stat. Comput. Simul. 90 (2), 355-389, 2020.
  • [32] R.M. Juvairiyya and P. Anilkumar, Estimation of stress-strength reliability for the Pareto distribution based on upper record values, Statistica 78 (4), 397-409, 2018.
  • [33] A.C. Kimber, Exploratory data analysis for possibly censored data from skewed distributions, J. Roy. Statist. Soc. Ser. C 39 (1), 21-30, 1990.
  • [34] F. Kızılaslan and M. Nadar, Statistical inference of $ P (X< Y) $ for the Burr Type XII distribution based on records, Hacet. J. Math. Stat. 46 (4), 713-742, 2017.
  • [35] A.O. Langlands, S.J. Pocock, G.R. Kerr and S.M. Gore, Long-term survival of patients with breast cancer: a study of the curability of the disease, Br. Med. J. 2 (6200), 1247- 1251, 1979.
  • [36] J.F. Lawless, Statistical Models and Methods for Lifetime Data, 2nd Edition, Hoboken: John Wiley & Sons, 2003.
  • [37] I.S. Mabrouk, Statistical inference for the parameter of the inverse Lindley distribution based on imprecise data with simulation study, Int. J. Contemp. Math. Sci. 14 (4), 151-161, 2019.
  • [38] W.W. Maennig, Bemarken zur Beurteilung des dauerschwring Festigkeitsverhaltens von Stahl und einige Untersuchungen zer Bestimmig des Dauerfestigkeitbereichs, Materialpruf 12, 124-131, 1967.
  • [39] M.A.W. Mahmoud, R.M. El-Sagheer, A.A. Soliman and A.H. Abd Ellah, Bayesian estimation of $P [Y< X]$ based on record values from the Lomax distribution and MCMC technique, J. Mod. Appl. Stat. Methods 15 (1), 488-510, 2016.
  • [40] J. Mazucheli, L.B. Fernandes and R.P. de Oliveira, LindleyR: The Lindley distribution and its modifications, R package version 1.0.0, https://CRAN.R-project.org/ package=LindleyR, 2016.
  • [41] O. Mersmann, H. Trautmann, D. Steuer and B. Bornkamp, truncnorm: Truncated normal distribution. R package version 1.0-8, https://CRAN.R-project.org/ package=truncnorm, 2018.
  • [42] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller, Equations of state calculations by fast computing machine, J. Chem. Phys. 21 (6), 1087- 1092, 1953.
  • [43] M. Nadar and F. Kızılaslan, Classical and Bayesian estimation of $P (X< Y)$ using upper record values from Kumaraswamy’s distribution, Statist. Papers 55 (3), 751-783, 2014.
  • [44] Z. Pakdaman and R. Alizadeh Noughabi, On the study of the stress-strength reliability in Weibull-F models, Hacet. J. Math. Stat. 53 (1), 269-288, 2024.
  • [45] M. Plummer, N. Best, K. Cowles and K. Vines, CODA: Convergence diagnosis and output analysis for MCMC, R News 6 (1), 7-11, 2006.
  • [46] M. Plummer, N. Best, K. Cowles, K. Vines, D. Sarkar, D. Bates, R. Almond and A. Magnusson, coda: Output analysis and diagnostics for MCMC. R package version 0.19-2, https://CRAN.R-project.org/package=coda, 2018.
  • [47] R Core Team, A Language and Envirenment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2020.
  • [48] M. Ramezani, A. Joukar and S.M.T.K. MirMostafaee, Estimation of the stressstrength parameter for a decreasing failure rate model based on ranked set samples. J. Test. Eval. 52 (6), https://doi.org/10.1520/JTE20240072, 2024.
  • [49] P.L. Ramos, F. Louzada, T.K. Shimizu and A.O. Luiz, The inverse weighted Lindley distribution: Properties, estimation and an application on a failure time data, Comm. Statist. Theory Methods 48 (10), 2372-2389, 2019.
  • [50] C.P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd Edition, New York: Springer, 2004.
  • [51] S. Sen, A.Z. Afify, H. Al-Mofleh and M. Ahsanullah, The quasi xgamma-geometric distribution with application in medicine, Filomat 33 (16), 5291-5330, 2019.
  • [52] J. Shao, Mathematical Statistics, 2nd Edition, New York: Springer, 2003.
  • [53] V.K. Sharma, S.K. Singh, U. Singh and V. Agiwal, The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data, J. Ind. Prod. Eng. 32 (3), 162-173, 2015.
  • [54] W. Stute, W.G. Manteiga and M.P. Quindimil, Bootstrap based goodness-of-fit-tests, Metrika 40 (1), 243-56, 1993.
  • [55] C. Tanis, B. Saraçoglu, A. Asgharzadeh and M. Abdi, Estimation of $Pr (X< Y)$ for exponential power records, Hacet. J. Math. Stat. 52 (2), 499-511, 2023.
  • [56] B. Tarvirdizade and M. Ahmadpour, Estimation of the stress-strength reliability for the two-parameter bathtub-shaped lifetime distribution based on upper record values, Stat. Methodol. 31, 58-72, 2016.
  • [57] B. Tarvirdizade and H. Kazemzadeh Gharehchobogh, Inference on $Pr (X> Y)$ based on record values from the Burr Type X distribution, Hacet. J. Math. Stat. 45 (1), 267-278, 2016.
  • [58] L. Tierney and J.B. Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc. 81 (393), 82-86, 1986.
  • [59] A. Tripathi, U. Singh and S.K. Singh, Estimation of $P (X< Y)$ for Gompertz distribution based on upper records, Int. J. Model. Simul. 42 (3), 388-399, 2022.
  • [60] H.R. Varian, A Bayesian approach to real estate assessment, in: Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage, Eds. S.E. Fienberg and A. Zellner, North-Holland Publishing Company, Amsterdam, 195-208, 1975.
  • [61] L.A. Wasserman, All of Statistics: A Concise Course in Statistical Inference, New York: Springer, 2004.
  • [62] A.C.M. Wong, and Y.Y. Wu, A note on interval estimation of $P (X< Y)$ using lower record data from the generalized exponential distribution, Comput. Statist. Data Anal. 53 (10), 3650-3658, 2009.
  • [63] A. Zellner, Bayesian estimation and prediction using asymmetric loss functions, J. Amer. Statist. Assoc. 81 (394), 446-451, 1986.
Year 2025, Volume: 54 Issue: 1, 291 - 318, 28.02.2025
https://doi.org/10.15672/hujms.1489598

Abstract

References

  • [1] G.A. Abd-Elmougod and H.H. Abu-Zinadah, Estimation of $P (Y<X)$ for twoparameter bathtub shape distribution using records: Bayesian and non-Bayesian approaches, J. Comput. Theor. Nanosci. 14 (2), 1127-1135, 2017.
  • [2] J. Albert, Bayesian Computation with R, 2nd Edition, Dordrecht: Springer, 2009.
  • [3] R. Alotaibi, M. Nassar and A. Elshahhat, Statistical analysis of inverse Lindley data using adaptive Type-II progressively hybrid censoring with applications, Axioms 12 (5), 427, 2023.
  • [4] B.C. Arnold, N. Balakrishnan and H.N. Nagaraja, Records, New York: John Wiley & Sons, 1998.
  • [5] A. Asgharzadeh, M. Alizadeh and M.Z. Raqab, Inverse Lindley distribution: different methods for estimating their PDF and CDF, J. Stat. Comput. Simul. 94 (3), 604-623, 2024.
  • [6] A. Asgharzadeh, R. Valiollahi and M.Z. Raqab, Estimation of $Pr (Y<X)$ for the two-parameter generalized exponential records, Comm. Statist. Simulation Comput. 46 (1), 379-394, 2017.
  • [7] A. Baklizi, Estimation of $Pr (X<Y)$ using record values in the one and two parameter exponential distributions, Comm. Statist. Theory Methods 37 (5), 692-698, 2008.
  • [8] A. Baklizi, Likelihood and Bayesian estimation of $Pr (X<Y)$ using lower record values from the generalized exponential distribution, Comput. Statist. Data Anal. 52 (7), 3468-3473, 2008.
  • [9] A. Baklizi, Bayesian inference for $Pr (Y<X)$ in the exponential distribution based on records, Appl. Math. Model. 38 (5-6), 1698-1709, 2014.
  • [10] A. Baklizi, Interval estimation of the stress-strength reliability in the two-parameter exponential distribution based on records, J. Stat. Comput. Simul. 84 (12), 2670-2679, 2014.
  • [11] S. Basu, S.K. Singh and U. Singh, Parameter estimation of inverse Lindley distribution for Type-I censored data, Comput. Statist. 32 (1), 367-385, 2017.
  • [12] S. Basu, S.K. Singh and U. Singh, Estimation of inverse Lindley distribution using product of spacings function for hybrid censored data, Methodol. Comput. Appl. Probab. 21 (4), 1377-1394, 2019.
  • [13] S. Bennett, Log-logistic regression models for survival data, J. Roy. Statist. Soc. Ser. C 32 (2), 165-171, 1983.
  • [14] R. Calabria and G. Pulcini, An engineering approach to Bayes estimation for the Weibull distribution, Microelectron. Reliab. 34 (5), 789-802, 1994.
  • [15] R. Calabria and G. Pulcini, Point estimation under asymmetric loss functions for left-truncated exponential samples, Comm. Statist. Theory Methods 25 (3), 585-600, 1996.
  • [16] R. Castro, Lectures 2 and 3 - Goodness-of-fit (GoF) Tests, 2013, https://rmcastro. win.tue.nl/AppStat2013/files/lectures23.pdf.
  • [17] A. Chaturvedi, S.K. Singh and U. Singh, Maximum product spacings estimator for fuzzy data using inverse Lindley distribution, Austrian J. Stat. 52 (2), 86-103, 2023.
  • [18] M.H. Chen and Q.M. Shao, Monte Carlo estimation of Bayesian credible and HPD intervals, J. Comput. Graph. Statist. 8 (1), 69-92, 1999.
  • [19] J.Y. Chiang, N. Jiang, T.R. Tsai and Y.L. Lio, Inference of $\delta= P (X< Y)$ for Burr XII distributions with record samples, Comm. Statist. Simulation Comput. 47 (3), 822-838, 2018.
  • [20] J.D. Church and B. Harris, The estimation of reliability from stress-strength relationships, Technometrics 12 (1), 49-54, 1970.
  • [21] F. Çiftci, B. Saraçoglu, N. Akdam and Y. Akdogan, Estimation of stress-strength reliability for generalized Gompertz distribution under progressive type-II censoring, Hacet. J. Math. Stat. 52 (5), 1379-1395, 2023.
  • [22] M.J. Crowder, Tests for a family of survival models based on extremes, in: Recent Advances in Reliability Theory: Methodology, Practice, and Inference, Eds. N. Limnios and M. Nikulin, Birkhäuser, Boston, 307-321, 2000.
  • [23] A.C. Davison and D.V. Hinkley, Bootstrap Methods and Their Application, Cambridge: Cambridge University Press, 1997.
  • [24] S. Dey, M. Nassar, D. Kumar, A. Alzaatreh and M.H. Tahir, A new lifetime distribution with decreasing and upside-down bathtub-shaped hazard rate function, Statistica 79 (4), 399-426, 2019.
  • [25] S. Dey and M. Saha, Bootstrap confidence intervals of the difference between two generalized process capability indices for inverse Lindley distribution, Life Cycle Reliab. Saf. Eng. 7 (2), 89-96, 2018.
  • [26] B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans, CBMS-NSF Regional Conference Series in Applied Mathematics 38, Philadelphia: Society for Industrial and Applied Mathematics (SIAM), 1982.
  • [27] B. Etemad Golestani, E. Ormoz and S.M.T.K. MirMostafaee, Statistical inference for the inverse Lindley distribution based on lower record values, REVSTAT, 2024, https://revstat.ine.pt/index.php/REVSTAT/article/view/559.
  • [28] M.K. Hassan, M.I. Alohali and F.A. Alojail, A new application of $R= P [Y< X]$ for the inverse Lindley distribution using ranked set sampling, J. Stat. Manag. Syst. 24 (8), 1713-1731, 2021.
  • [29] W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1), 97-109, 1970.
  • [30] S. Joo and J. Mi, Some properties of hazard rate functions of systems with two components, J. Statist. Plann. Inference 140 (2), 444-453, 2010.
  • [31] A. Joukar, M. Ramezani and S.M.T.K. MirMostafaee, Estimation of $P (X> Y)$ for the power Lindley distribution based on progressively type II right censored samples, J. Stat. Comput. Simul. 90 (2), 355-389, 2020.
  • [32] R.M. Juvairiyya and P. Anilkumar, Estimation of stress-strength reliability for the Pareto distribution based on upper record values, Statistica 78 (4), 397-409, 2018.
  • [33] A.C. Kimber, Exploratory data analysis for possibly censored data from skewed distributions, J. Roy. Statist. Soc. Ser. C 39 (1), 21-30, 1990.
  • [34] F. Kızılaslan and M. Nadar, Statistical inference of $ P (X< Y) $ for the Burr Type XII distribution based on records, Hacet. J. Math. Stat. 46 (4), 713-742, 2017.
  • [35] A.O. Langlands, S.J. Pocock, G.R. Kerr and S.M. Gore, Long-term survival of patients with breast cancer: a study of the curability of the disease, Br. Med. J. 2 (6200), 1247- 1251, 1979.
  • [36] J.F. Lawless, Statistical Models and Methods for Lifetime Data, 2nd Edition, Hoboken: John Wiley & Sons, 2003.
  • [37] I.S. Mabrouk, Statistical inference for the parameter of the inverse Lindley distribution based on imprecise data with simulation study, Int. J. Contemp. Math. Sci. 14 (4), 151-161, 2019.
  • [38] W.W. Maennig, Bemarken zur Beurteilung des dauerschwring Festigkeitsverhaltens von Stahl und einige Untersuchungen zer Bestimmig des Dauerfestigkeitbereichs, Materialpruf 12, 124-131, 1967.
  • [39] M.A.W. Mahmoud, R.M. El-Sagheer, A.A. Soliman and A.H. Abd Ellah, Bayesian estimation of $P [Y< X]$ based on record values from the Lomax distribution and MCMC technique, J. Mod. Appl. Stat. Methods 15 (1), 488-510, 2016.
  • [40] J. Mazucheli, L.B. Fernandes and R.P. de Oliveira, LindleyR: The Lindley distribution and its modifications, R package version 1.0.0, https://CRAN.R-project.org/ package=LindleyR, 2016.
  • [41] O. Mersmann, H. Trautmann, D. Steuer and B. Bornkamp, truncnorm: Truncated normal distribution. R package version 1.0-8, https://CRAN.R-project.org/ package=truncnorm, 2018.
  • [42] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller, Equations of state calculations by fast computing machine, J. Chem. Phys. 21 (6), 1087- 1092, 1953.
  • [43] M. Nadar and F. Kızılaslan, Classical and Bayesian estimation of $P (X< Y)$ using upper record values from Kumaraswamy’s distribution, Statist. Papers 55 (3), 751-783, 2014.
  • [44] Z. Pakdaman and R. Alizadeh Noughabi, On the study of the stress-strength reliability in Weibull-F models, Hacet. J. Math. Stat. 53 (1), 269-288, 2024.
  • [45] M. Plummer, N. Best, K. Cowles and K. Vines, CODA: Convergence diagnosis and output analysis for MCMC, R News 6 (1), 7-11, 2006.
  • [46] M. Plummer, N. Best, K. Cowles, K. Vines, D. Sarkar, D. Bates, R. Almond and A. Magnusson, coda: Output analysis and diagnostics for MCMC. R package version 0.19-2, https://CRAN.R-project.org/package=coda, 2018.
  • [47] R Core Team, A Language and Envirenment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2020.
  • [48] M. Ramezani, A. Joukar and S.M.T.K. MirMostafaee, Estimation of the stressstrength parameter for a decreasing failure rate model based on ranked set samples. J. Test. Eval. 52 (6), https://doi.org/10.1520/JTE20240072, 2024.
  • [49] P.L. Ramos, F. Louzada, T.K. Shimizu and A.O. Luiz, The inverse weighted Lindley distribution: Properties, estimation and an application on a failure time data, Comm. Statist. Theory Methods 48 (10), 2372-2389, 2019.
  • [50] C.P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd Edition, New York: Springer, 2004.
  • [51] S. Sen, A.Z. Afify, H. Al-Mofleh and M. Ahsanullah, The quasi xgamma-geometric distribution with application in medicine, Filomat 33 (16), 5291-5330, 2019.
  • [52] J. Shao, Mathematical Statistics, 2nd Edition, New York: Springer, 2003.
  • [53] V.K. Sharma, S.K. Singh, U. Singh and V. Agiwal, The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data, J. Ind. Prod. Eng. 32 (3), 162-173, 2015.
  • [54] W. Stute, W.G. Manteiga and M.P. Quindimil, Bootstrap based goodness-of-fit-tests, Metrika 40 (1), 243-56, 1993.
  • [55] C. Tanis, B. Saraçoglu, A. Asgharzadeh and M. Abdi, Estimation of $Pr (X< Y)$ for exponential power records, Hacet. J. Math. Stat. 52 (2), 499-511, 2023.
  • [56] B. Tarvirdizade and M. Ahmadpour, Estimation of the stress-strength reliability for the two-parameter bathtub-shaped lifetime distribution based on upper record values, Stat. Methodol. 31, 58-72, 2016.
  • [57] B. Tarvirdizade and H. Kazemzadeh Gharehchobogh, Inference on $Pr (X> Y)$ based on record values from the Burr Type X distribution, Hacet. J. Math. Stat. 45 (1), 267-278, 2016.
  • [58] L. Tierney and J.B. Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc. 81 (393), 82-86, 1986.
  • [59] A. Tripathi, U. Singh and S.K. Singh, Estimation of $P (X< Y)$ for Gompertz distribution based on upper records, Int. J. Model. Simul. 42 (3), 388-399, 2022.
  • [60] H.R. Varian, A Bayesian approach to real estate assessment, in: Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage, Eds. S.E. Fienberg and A. Zellner, North-Holland Publishing Company, Amsterdam, 195-208, 1975.
  • [61] L.A. Wasserman, All of Statistics: A Concise Course in Statistical Inference, New York: Springer, 2004.
  • [62] A.C.M. Wong, and Y.Y. Wu, A note on interval estimation of $P (X< Y)$ using lower record data from the generalized exponential distribution, Comput. Statist. Data Anal. 53 (10), 3650-3658, 2009.
  • [63] A. Zellner, Bayesian estimation and prediction using asymmetric loss functions, J. Amer. Statist. Assoc. 81 (394), 446-451, 1986.
There are 63 citations in total.

Details

Primary Language English
Subjects Statistical Theory
Journal Section Statistics
Authors

Bahareh Etemad Golestani 0009-0000-6360-7422

Ehsan Ormoz 0000-0003-3557-5755

S.m.t.k. Mirmostafaee 0000-0003-2796-4427

Early Pub Date January 3, 2025
Publication Date February 28, 2025
Submission Date May 25, 2024
Acceptance Date December 24, 2024
Published in Issue Year 2025 Volume: 54 Issue: 1

Cite

APA Etemad Golestani, B., Ormoz, E., & Mirmostafaee, S. (2025). On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values. Hacettepe Journal of Mathematics and Statistics, 54(1), 291-318. https://doi.org/10.15672/hujms.1489598
AMA Etemad Golestani B, Ormoz E, Mirmostafaee S. On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values. Hacettepe Journal of Mathematics and Statistics. February 2025;54(1):291-318. doi:10.15672/hujms.1489598
Chicago Etemad Golestani, Bahareh, Ehsan Ormoz, and S.m.t.k. Mirmostafaee. “On the Estimation of the Stress-Strength Parameter for the Inverse Lindley Distribution Based on Lower Record Values”. Hacettepe Journal of Mathematics and Statistics 54, no. 1 (February 2025): 291-318. https://doi.org/10.15672/hujms.1489598.
EndNote Etemad Golestani B, Ormoz E, Mirmostafaee S (February 1, 2025) On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values. Hacettepe Journal of Mathematics and Statistics 54 1 291–318.
IEEE B. Etemad Golestani, E. Ormoz, and S. Mirmostafaee, “On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values”, Hacettepe Journal of Mathematics and Statistics, vol. 54, no. 1, pp. 291–318, 2025, doi: 10.15672/hujms.1489598.
ISNAD Etemad Golestani, Bahareh et al. “On the Estimation of the Stress-Strength Parameter for the Inverse Lindley Distribution Based on Lower Record Values”. Hacettepe Journal of Mathematics and Statistics 54/1 (February 2025), 291-318. https://doi.org/10.15672/hujms.1489598.
JAMA Etemad Golestani B, Ormoz E, Mirmostafaee S. On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values. Hacettepe Journal of Mathematics and Statistics. 2025;54:291–318.
MLA Etemad Golestani, Bahareh et al. “On the Estimation of the Stress-Strength Parameter for the Inverse Lindley Distribution Based on Lower Record Values”. Hacettepe Journal of Mathematics and Statistics, vol. 54, no. 1, 2025, pp. 291-18, doi:10.15672/hujms.1489598.
Vancouver Etemad Golestani B, Ormoz E, Mirmostafaee S. On the estimation of the stress-strength parameter for the inverse Lindley distribution based on lower record values. Hacettepe Journal of Mathematics and Statistics. 2025;54(1):291-318.