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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/41239


    Title: Point and interval estimation for Gaussian distribution, based on progressively Type-II censored samples
    Authors: Balakrishnan, N.;Kannan, N.;林千代;Lin, C. T.;Ng, H. K. T.
    Contributors: 淡江大學數學學系
    Keywords: Gaussian distribution;Hazard function;Maximum likelihood estimator;Monte Carlo simulation;Optimal censoring scheme;Pivotal quantity;Progressive type-II censoring;Statistical-confidence interval
    Date: 2003-03
    Issue Date: 2010-01-28 07:02:55 (UTC+8)
    Publisher: Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: The likelihood equations based on a progressively Type-II censored sample from a Gaussian distribution do not provide explicit solutions in any situation except the complete sample case. This paper examines numerically the bias and mean square error of the MLE, and demonstrates that the probability coverages of the pivotal quantities (for location and scale parameters) based on asymptotic s-normality are unsatisfactory, and particularly so when the effective sample size is small. Therefore, this paper suggests using unconditional simulated percentage points of these pivotal quantities for constructing s-confidence intervals. An approximation of the Gaussian hazard function is used to develop approximate estimators which are explicit and are almost as efficient as the MLE in terms of bias and mean square error; however, the probability coverages of the corresponding pivotal quantities based on asymptotic s-normality are also unsatisfactory. A wide range of sample sizes and progressive censoring schemes are used in this study.
    Relation: IEEE Transactions on Reliability 52(1), pp.90-95
    DOI: 10.1109/TR.2002.805786
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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