In the present work, a weighted maximum likelihood method (WMLM) is proposed to obtain robust estimates of experimental data containing outliers. The method allows asymptotically effective robust unbiased estimates to be obtained in the presence of not only external, but also internal asymmetric and symmetric outliers. Algorithms for obtaining robust WMLM estimates are considered at the parametric level of aprioristic uncertainty. It is demonstrated that these estimates converge to the maximum likelihood estimates of a heterogeneous data sample for each distribution within the Tukey supermodel.

中文翻译：

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异构实验数据的稳健参数估计

在目前的工作中，提出了一种加权最大似然法（WMLM）来获得包含异常值的实验数据的稳健估计。该方法允许在不仅存在外部而且存在内部不对称和对称异常值的情况下获得渐近有效的稳健无偏估计。在先验不确定性的参数级别考虑用于获得稳健 WMLM 估计的算法。证明这些估计收敛到 Tukey 超模型中每个分布的异构数据样本的最大似然估计。