描述M-estimators

IF 2.4 2区数学 Q2 BIOLOGY

Biometrika Pub Date : 2022-08-17 DOI:10.1093/biomet/asad026

Timo Dimitriadis, Tobias Fissler, Johanna F. Ziegel

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引用次数: 5

摘要

通过将预测评估的一致损失函数理论与M-估计理论形式化地联系起来，我们刻画了一般泛函的半参数模型的全类M-估计量。这一新颖的表征结果使我们能够利用现有的损失函数结果，这些损失函数是从估计理论中的预测评估文献中已知的。我们举例说明了稳健、有效、等变和帕累托最优M-估计领域的有利含义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Characterizing M-estimators

We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M-estimation. This novel characterization result allows us to leverage existing results on loss functions known from the literature on forecast evaluation in estimation theory. We exemplify advantageous implications for the fields of robust, efficient, equivariant and Pareto-optimal M-estimation.

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来源期刊

Biometrika 生物-生物学

CiteScore

5.50

自引率

3.70%

发文量

审稿时长

6-12 weeks

期刊介绍： Biometrika is primarily a journal of statistics in which emphasis is placed on papers containing original theoretical contributions of direct or potential value in applications. From time to time, papers in bordering fields are also published.