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渐近最佳线性估计量最优分位点选择的统一方法AI 解读

Due to its computational simplicity and efficiency, asymptotically best linear estimates (ABLE) based on order statistics selected from a complete, or censored, sample are of particular importance. This article deals with a unified method for finding the optimum spacing (eventually the optimum rank of the order statistics) for the ABLE. Application of the method estimating one of the two (location and scale) parameters of various distributions is also discussed.

多重删失 Logistic 数据下均值渐近最优线性估计的算法AI 解读

An algorithm is considered for allocating an optimum spacing for the asymptotically best linear estimate (ABLE) of the mean μ, when the standard deviation πσ 0/√3 of the logistic distribution is known, based on k order statistics selected from a large multiply censored sample. This algorithm can be applied to problems of estimating the location parameter of the normal, Cauchy and extreme-value distribution when the scale parameter is known and estimating the scale parameter…