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Discussion of 'The power of monitoring: how to make the most of a contaminated multivariate sample' by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini

机译:Andrea Cerioli,Marco Riani,Anthony C. Atkinson和Aldo Corbellini讨论了“监视的力量:如何充分利用受污染的多元样本”

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This paper discusses the contribution of Cerioli et al. (Stat Methods Appl, 2018), where robust monitoring based on high breakdown point estimators is proposed for multivariate data. The results follow years of development in robust diagnostic techniques. We discuss the issues of extending data monitoring to other models with complex structure, e.g. factor analysis, mixed linear models for which S and MM-estimators exist or deviating data cells. We emphasise the importance of robust testing that is often overlooked despite robust tests being readily available once S and MM-estimators have been defined. We mention open questions like out-of-sample inference or big data issues that would benefit from monitoring.
机译:本文讨论了Cerioli等人的贡献。 (Stat Methods Appl,2018),其中针对多变量数据提出了基于高击穿点估计量的鲁棒监视。该结果遵循了强大的诊断技术的多年发展。我们讨论了将数据监视扩展到具有复杂结构的其他模型的问题,例如因子分析,存在S和MM估计量或数据单元偏差的混合线性模型。我们强调了健壮测试的重要性,尽管一旦定义了S和MM估计量,健壮测试就很容易获得,但它经常被忽略。我们提到了一些未解决的问题,例如样本外推断或大数据问题,这些问题将受益于监控。

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