淡江大學機構典藏:Item 987654321/41198
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/41198


    Title: Inference for Log-Gamma distribution based on progressively Type-II censored data
    Authors: 林千代;Lin, Chien-tai;Wu, S. J. S.;Balakrishnan, N
    Contributors: 淡江大學數學學系
    Keywords: Approximate maximum likelihood estimators;EM algorithm;Extreme value distribution;Fisher information;Fixed-point iteration;Maximum likelihood estimators;Modified EM algorithm;Monte Carlo simulations;Newton–Raphson method;Normal distribution;Pivotal quantities;Probability coverages
    Date: 2006-06
    Issue Date: 2010-01-28 06:54:47 (UTC+8)
    Publisher: Taylor & Francis
    Abstract: We discuss the maximum likelihood estimates (MLEs) of the parameters of the log-gamma distribution based on progressively Type-II censored samples. We use the profile likelihood approach to tackle the problem of the estimation of the shape parameter κ. We derive approximate maximum likelihood estimators of the parameters μ and σ and use them as initial values in the determination of the MLEs through the Newton–Raphson method. Next, we discuss the EM algorithm and propose a modified EM algorithm for the determination of the MLEs. A simulation study is conducted to evaluate the bias and mean square error of these estimators and examine their behavior as the progressive censoring scheme and the shape parameter vary. We also discuss the interval estimation of the parameters μ and σ and show that the intervals based on the asymptotic normality of MLEs have very poor probability coverages for small values of m. Finally, we present two examples to illustrate all the methods of inference discussed in this paper.
    Relation: Communications in Statistics: Theory and Methods 35(7), pp.1271-1292
    DOI: 10.1080/03610920600692789
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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