淡江大學機構典藏:Item 987654321/41343
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    题名: Inference for the extreme value distribution under progressive type-II censoring
    作者: Balakrishnan, N.;Kannan, N.;Lin, C. T.;林千代;Wu, Sam S. J.
    贡献者: 淡江大學數學學系
    关键词: Maximum likelihood estimator;Monte Carlo simulation;Pivotal quantity;Progressive Type-II censoring
    日期: 2004-01
    上传时间: 2010-01-28 07:20:57 (UTC+8)
    出版者: Taylor & Francis
    摘要: The extreme value distribution has been extensively used to model natural phenomena such as rainfall and floods, and also in modeling lifetimes and material strengths. Maximum likelihood estimation (MLE) for the parameters of the extreme value distribution leads to likelihood equations that have to be solved numerically, even when the complete sample is available. In this paper, we discuss point and interval estimation based on progressively Type-II censored samples. Through an approximation in the likelihood equations, we obtain explicit estimators which are approximations to the MLEs. Using these approximate estimators as starting values, we obtain the MLEs using an iterative method and examine numerically their bias and mean squared error. The approximate estimators compare quite favorably to the MLEs in terms of both bias and efficiency. Results of the simulation study, however, show that the probability coverages of the pivotal quantities (for location and scale parameters) based on asymptotic normality are unsatisfactory for both these estimators and particularly so when the effective sample size is small. We, therefore, suggest the use of unconditional simulated percentage points of these pivotal quantities for the construction of confidence intervals. The results are presented for a wide range of sample sizes and different progressive censoring schemes. We conclude with an illustrative example.
    關聯: Journal of Statistical Computation and Simulation 74(1), pp.25-45
    DOI: 10.1080/0094965031000105881
    显示于类别:[數學學系暨研究所] 期刊論文

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