Kundu, Debasis; Pradhan, Biswabrata Estimating the parameters of the generalized exponential distribution in presence of hybrid censoring. (English) Zbl 1167.62078 Commun. Stat., Theory Methods 38, No. 12, 2030-2041 (2009). Summary: The two most popular censoring schemes are Type-I and Type-II censoring schemes. Hybrid censoring schemes are a mixture of Type-I and Type-II censoring schemes. We mainly consider the analysis of hybrid censored data when the lifetime distribution of the individual item is a two-parameter generalized exponential distribution. It is observed that the maximum likelihood estimators cannot be obtained in closed form. We propose to use the EM algorithm to compute the maximum likelihood estimators. We obtain the observed Fisher information matrix using the missing information principle and this can be used for constructing asymptomatic confidence intervals. We also obtain the Bayes estimates of the unknown parameters under the assumption of independent gamma priors using the importance sampling procedure. One data set has been analyzed for illustrative purposes. Cited in 40 Documents MSC: 62N01 Censored data models 62F10 Point estimation 62N02 Estimation in survival analysis and censored data 62F15 Bayesian inference 65C60 Computational problems in statistics (MSC2010) Keywords:asymptotic distribution; EM algorithm; Fisher information matrix; importance sampling; maximum likelihood estimators; type-I censoring; type-II censoring PDFBibTeX XMLCite \textit{D. Kundu} and \textit{B. Pradhan}, Commun. Stat., Theory Methods 38, No. 12, 2030--2041 (2009; Zbl 1167.62078) Full Text: DOI References: [1] DOI: 10.1109/TR.2008.916890 · doi:10.1109/TR.2008.916890 [2] DOI: 10.1080/03610928808829718 · Zbl 0644.62101 · doi:10.1080/03610928808829718 [3] DOI: 10.2307/1390921 · doi:10.2307/1390921 [4] DOI: 10.1007/BF02530502 · doi:10.1007/BF02530502 [5] DOI: 10.1007/BF02491461 · Zbl 0612.62134 · doi:10.1007/BF02491461 [6] Ebrahimi N., J. Statist. Plann. Infer. 23 pp 255– (1990) [7] DOI: 10.1109/24.126685 · Zbl 0743.62089 · doi:10.1109/24.126685 [8] DOI: 10.1214/aoms/1177728723 · Zbl 0058.35104 · doi:10.1214/aoms/1177728723 [9] DOI: 10.2307/2287779 · Zbl 0504.62087 · doi:10.2307/2287779 [10] DOI: 10.1080/03610929808832273 · Zbl 1008.62679 · doi:10.1080/03610929808832273 [11] DOI: 10.1111/1467-842X.00072 · Zbl 1007.62503 · doi:10.1111/1467-842X.00072 [12] DOI: 10.1002/1521-4036(200102)43:1<117::AID-BIMJ117>3.0.CO;2-R · Zbl 0997.62076 · doi:10.1002/1521-4036(200102)43:1<117::AID-BIMJ117>3.0.CO;2-R [13] DOI: 10.1016/j.jspi.2007.03.030 · Zbl 1119.62011 · doi:10.1016/j.jspi.2007.03.030 [14] DOI: 10.1080/02664769623964 · doi:10.1080/02664769623964 [15] DOI: 10.1016/j.jspi.2006.06.043 · Zbl 1120.62081 · doi:10.1016/j.jspi.2006.06.043 [16] DOI: 10.1016/j.csda.2007.06.004 · Zbl 1452.62182 · doi:10.1016/j.csda.2007.06.004 [17] Lawless J. F., Statistical Models and Methods for Lifetime Data (1982) · Zbl 0541.62081 [18] Louis T. A., J. Roy. Statist. Soc. Ser. B 44 pp 226– (1982) [19] Reliability Design Qualifications and Production Acceptance Test, Exponential Distribution (1977) [20] DOI: 10.1016/S0167-9473(01)00091-3 · Zbl 0993.62085 · doi:10.1016/S0167-9473(01)00091-3 [21] DOI: 10.1080/00949650412331299166 · Zbl 1076.62025 · doi:10.1080/00949650412331299166 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.