Estimation for partially observed left truncation and right censored competing risks data from a generalized inverted exponential distribution with illustrations

References

• Abouammoh, A. M., & Alshingiti, A. (2009). Reliability estimation of generalized inverted exponential distribution. Journal of Statistical Computation and Simulation, 79(11), 1301–1315. https://doi.org/10.1080/00949650802261095
   Web of Science ®Google Scholar
• Ahmadi, M. V., & Doostparast, M. (2022). Bayesian analysis of the lifetime performance index on the basis of progressively censored Weibull observations. Quality Technology & Quantitative Management, 19(2), 187–214. https://doi.org/10.1080/16843703.2021.1963032
   Web of Science ®Google Scholar
• Asadi, S., & Panahi, H. (2022). Estimation of stress-strength reliability based on censored data and its evaluation for coating processes. Quality Technology & Quantitative Management, 19(3), 379–401. https://doi.org/10.1080/16843703.2021.2001129
   Web of Science ®Google Scholar
• Balakrishnan, N., & Cramer, E. (2014). The art of progressive censoring. Birkhauser. https://doi.org/10.1007/978-0-8176-4807-7
   Google Scholar
• Balakrishnan, N., & Mitra, D. (2011). Likelihood inference for log-normal data with left-truncation and right censoring with an illustration. Journal of Statistical Planning and Inference, 141(11), 3536–3553. https://doi.org/10.1016/j.jspi.2011.05.007
   Web of Science ®Google Scholar
• Balakrishnan, N., & Mitra, D. (2012). Left truncated and right censored Weibull data and likelihood inference with an illustration. Computational Statistics & Data Analysis, 56(12), 4011–4025. https://doi.org/10.1016/j.csda.2012.05.004
   Web of Science ®Google Scholar
• Balakrishnan, N., & Mitra, D. (2013). Likelihood inference based on left truncated and right censored data from a gamma distribution. IEEE Transactions on Reliability, 62(3), 679–688. https://doi.org/10.1109/TR.2013.2273039
   Web of Science ®Google Scholar
• Bdair, O. M., Abu Awwad, R. R., Abufoudeh, G. K., & Naser, M. F. M. (2020). Estimation and prediction for ?exible Weibull distribution based on progressive type II censored data. Communications in Mathematics and Statistics, 8(3), 255–277. https://doi.org/10.1007/s40304-018-00173-0
   Web of Science ®Google Scholar
• Chen, Q., & Gui, W. (2022). Statistical inference of the generalized inverted exponential distribution under joint progressively type-II censoring. Entropy, 24, 576. https://doi.org/10.3390/e24050576
   PubMed Web of Science ®Google Scholar
• Crowder, M. J. (2001). Classical competing risks. Chapman & Hall. https://doi.org/10.1201/9781420035902
   Google Scholar
• Dey, S., Dey, T., & Luckett, D. J. (2016). Statistical inference for the generalized inverted exponential distribution based on upper record values. Mathematics and Computers in Simulation, 20, 64–78. https://doi.org/10.1016/j.matcom.2015.06.012
   Google Scholar
• Fu, J., Tang, Y., & Guan, Q. (2014). Objective Bayesian analysis for recurrent events in presence of competing risks. Quality Technology & Quantitative Management, 11(3), 265–279. https://doi.org/10.1080/16843703.2014.11673344
   Web of Science ®Google Scholar
• Geskus, R. B. (2016). Data analysis with competing risks and intermediate states. CRC Press. https://doi.org/10.1201/b18695
   Google Scholar
• Ghitany, M., Al-Jarallah, R., & Balakrishnan, N. (2013). On the existence and uniqueness of the mles of the parameters of a general class of exponentiated distributions. Statistics, 47(3), 605–612. https://doi.org/10.1080/02331888.2011.614950
   Web of Science ®Google Scholar
• Gilks, W. R., & Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41(2), 337–348. https://doi.org/10.2307/2347565
   Web of Science ®Google Scholar
• Hassan, A. S., Al-Omari, A., & Nagy, H. F. (2021). Stress–strength reliability for the generalized inverted exponential distribution using MRSS. Iranian Journal of Science & Technology, Transactions A: Science, 45(2), 641–659. https://doi.org/10.1007/s40995-020-01033-9
   Web of Science ®Google Scholar
• Hong, Y., Meeker, W. Q., & McCalley, J. D. (2009). Prediction of remaining life of power transformers based on left truncated and right censored lifetime data. The Annals of Applied Statistics, 3(2), 857–879. https://doi.org/10.1214/00-AOAS231
   Web of Science ®Google Scholar
• Hwang, Y. T., & Wang, C. C. (2008). A goodness of ?t test for left-truncated and right-censored data. Statistics & Probability Letters, 78(15), 2420–2425. https://doi.org/10.1016/j.spl.2008.02.035
   Web of Science ®Google Scholar
• Klein, J. P., & Moeschberger, M. L. (2003). Survival analysis techniques for censored and truncated data (2nd ed.). Springer. https://doi.org/10.1007/b97377
   Google Scholar
• Krishna, H., Dube, M., & Renu, G. R. (2017). Estimation of P(Y<X) for progressively first-failure¬ censored generalized inverted exponential distribution. Journal of Statistical Computation and Simulation, 87(11), 2274–2289. https://doi.org/10.1080/00949655.2017.1326119
   Web of Science ®Google Scholar
• Kundu, D., & Mitra, D. (2016). Bayesian inference of Weibull distribution based on left truncated and right censored data. Computational Statistics & Data Analysis, 99, 38–50. https://doi.org/10.1016/j.csda.2016.01.001
   Web of Science ®Google Scholar
• Kundu, D., Mitra, D., & Ganguly, A. (2017). Analysis of left truncated and right censored competing risks data. Computational Statistics & Data Analysis, 108, 12–26. https://doi.org/10.1016/j.csda.2016.10.020
   Web of Science ®Google Scholar
• Kundu, D., & Sarhan, A. M. (2006). Analysis of incomplete data in presence of competing risks among several groups. IEEE Transactions on Reliability, 55(2), 262–269. https://doi.org/10.1109/TR.2006.874919
   Web of Science ®Google Scholar
• Lawless, J. F. (2003). Statistical models and methods for lifetime data, 2ed. Wiley. https://doi.org/10.1002/9781118033005
   Google Scholar
• Luo, X., & Tsai, W. Y. (2009). Nonparametric estimation for right-censored length-biased data: A pseudo-partial likelihood approach. Biometrika, 96(4), 873–886. https://doi.org/10.1093/biomet/asp064
   Web of Science ®Google Scholar
• Lu, K., & Tsiatis, A. A. (2005). Comparison between two partial likelihood approaches for the competing risks model with missing cause of failure. Lifetime Data Analysis, 11(1), 29–40. https://doi.org/10.1007/s10985-004-5638-0
   PubMed Web of Science ®Google Scholar
• Mahto, A. K., Lodhi, C., Tripathi, Y. M., & Wang, L. (2022). On partially observed competing risk model under generalized progressive hybrid censoring for lomax distribution. Quality Technology & Quantitative Management, 19(5), 562–586. https://doi.org/10.1080/16843703.2022.2049507
   Web of Science ®Google Scholar
• Mondal, S., & Kundu, D. (2019). Point and interval estimation of Weibull parameters based on joint progressively censored data. Sankhya B, 81(1), 1–25. https://doi.org/10.1007/s13571-017-0134-1
   Google Scholar
• Nadarajah, S., & Kotz, S. (2000). Extreme value distributions: Theory and applications. Imperial College Press.
   Google Scholar
• Panahi, H., & Asadi, P. (2022). Estimating the parameters of a generalized inverted exponential distribution based on adaptive type II hybrid progressive censoring with application. Journal of Statistics and Management Systems, 25(2), 433–455. https://doi.org/10.1080/09720510.2021.1892259
   Web of Science ®Google Scholar
• Pena, E. A., & Gupta, A. K. (1990). Bayes estimation for the Marshall-Olkin exponential distribution. Journal of the Royal Statistical Society: Series B (Methodological), 52(2), 379–389. https://doi.org/10.1111/j.2517-6161.1990.tb01794.x
   Google Scholar
• Peng, M., Xiang, L., & Wang, S. (2018). Semiparametric regression analysis of clustered survival data with semi-competing risks. Computational Statistics & Data Analysis, 24, 53–70. https://doi.org/10.1016/j.csda.2018.02.003
   Google Scholar
• Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26(11), 2389–2430. https://doi.org/10.1002/sim.2712
   PubMed Web of Science ®Google Scholar
• Rafiee, K., Feng, Q., & Coit, D. W. (2017). Reliability assessment of competing risks with generalized mixed shock models. Reliability Engineering & System Safety, 159, 1–11. https://doi.org/10.1016/j.ress.2016.10.006
   Web of Science ®Google Scholar
• Samanta, D., & Kundu, D. (2021). Bayesian inference of a dependent competing risk data. Journal of Statistical Computation and Simulation, 91(15), 3069–3086. https://doi.org/10.1080/00949655.2021.1917575
   Web of Science ®Google Scholar
• Shih, J. H., & Emura, T. (2018). Likelihood-based inference for bivariate latent failure time models with competing risks under the generalized FGM copula. Computational Statistics, 331(3), 1293–1323. https://doi.org/10.1007/s00180-018-0804-0
   Google Scholar
• Sief, M., Liu, X., & Abd El-Raheemc, A. E. M. (2021). Inference for a constant-stress model under progressive type-I interval censored data from the generalized half-normal distribution. Journal of Statistical Computation and Simulation, 91(15), 3228–3253. https://doi.org/10.1080/00949655.2021.1925673
   Web of Science ®Google Scholar
• Tripathy, M. R. (2018). Improved estimation of common location of two exponential populations with order restricted scale parameters using censored samples. Communications in Statistics-Simulation and Computation, 47(9), 2800–2818. https://doi.org/10.1080/03610918.2017.1361974
   Web of Science ®Google Scholar
• Wang, L., Tripathi, Y. M., & Lodhi, C. (2020). Inference for Weibull competing risks model with partially observed failure causes under generalized progressive hybrid censoring. Journal of Computational and Applied Mathematics, 368, 112537. https://doi.org/10.1016/j.cam.2019.112537
   Web of Science ®Google Scholar