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    題名: Point and interval estimation for Gaussian distribution, based on progressively Type-II censored samples
    作者: Balakrishnan, N.;Kannan, N.;林千代;Lin, C. T.;Ng, H. K. T.
    貢獻者: 淡江大學數學學系
    關鍵詞: Gaussian distribution;Hazard function;Maximum likelihood estimator;Monte Carlo simulation;Optimal censoring scheme;Pivotal quantity;Progressive type-II censoring;Statistical-confidence interval
    日期: 2003-03
    上傳時間: 2010-01-28 07:02:55 (UTC+8)
    出版者: Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
    摘要: 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.
    關聯: IEEE Transactions on Reliability 52(1), pp.90-95
    DOI: 10.1109/TR.2002.805786
    顯示於類別:[數學學系暨研究所] 期刊論文

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