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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100144


    Title: On estimating parameters of a progressively censored lognormal distribution
    Authors: Singh, S.;Tripathi, Y. M.;Wu, S.-J.
    Contributors: 淡江大學統計學系
    Keywords: approximate maximum likelihood estimate;Bayes estimate;EM algorithm;Fisher information matrix;importance sampling;Lindley's method;maximum likelihood estimate;optimal censoring
    Date: 2015-04
    Issue Date: 2015-01-30 20:28:08 (UTC+8)
    Publisher: Abingdon: Taylor & Francis
    Abstract: We consider the problem of making statistical inference on unknown parameters of a lognormal distribution under the assumption that samples are progressively censored. The maximum likelihood estimates (MLEs) are obtained by using the expectation-maximization algorithm. The observed and expected Fisher information matrices are provided as well. Approximate MLEs of unknown parameters are also obtained. Bayes and generalized estimates are derived under squared error loss function. We compute these estimates using Lindley's method as well as importance sampling method. Highest posterior density interval and asymptotic interval estimates are constructed for unknown parameters. A simulation study is conducted to compare proposed estimates. Further, a data set is analysed for illustrative purposes. Finally, optimal progressive censoring plans are discussed under different optimality criteria and results are presented.
    Relation: Journal of Statistical Computation and Simulation 85(6), p.1071-1089
    DOI: 10.1080/00949655.2013.861838
    Appears in Collections:[統計學系暨研究所] 期刊論文

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