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Opportunities of the minimum Anderson-Darling estimator as a variant of the maximum likelihood method

机译:最小Anderson-Darling估计量的机会作为最大似然法的一种变体

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We reveal that the minimum Anderson-Darling (MAD) estimator is a variant of the maximum likelihood method. Furthermore, it is shown that the MAD estimator offers excellent opportunities for parameter estimation if there is no explicit formulation for the distribution model. The computation time for the MAD estimator with approximated cumulative distribution function is much shorter than that of the classical maximum likelihood method with approximated probability density function. Additionally, we research the performance of the MAD estimator for the generalized Pareto distribution and demonstrate a further advantage of the MAD estimator with an issue of seismic hazard analysis.
机译:我们揭示了最小Anderson-Darling(MAD)估计量是最大似然方法的一种变体。此外,它表明,如果没有针对分布模型的明确表述,则MAD估计器为参数估计提供了极好的机会。具有近似累积分布函数的MAD估计器的计算时间比具有近似概率密度函数的经典最大似然方法的计算时间短得多。此外,我们研究了MAD估计器对广义Pareto分布的性能,并证明了MAD估计器在地震危险性分析方面的进一步优势。

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