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- W1985383669 abstract "Abstract 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 of the exponential, Weibull and Pareto distributions when the location and shape parameters are known. When both μ and the scale parameter σ are unknown and one is mainly interested in estimating μ, it is demonstrated that the above spacing is reasonable." @default.
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- W1985383669 date "1974-12-01" @default.
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- W1985383669 title "An Algorithm for Determining the Asymptotically Best Linear Estimate of the Mean from Multiply Censored Logistic Data" @default.
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- W1985383669 doi "https://doi.org/10.1080/01621459.1974.10480248" @default.
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