The power of monitoring: how to make the most of a contaminated multivariate sample
The power of monitoring: how to make the most of a contaminated multivariate sample
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DOI：
10.1007/s10260-017-0409-8
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发表时间：
2018-12-01
影响因子：
1
通讯作者：
Corbellini, Aldo
中科院分区：
文献类型：
--
作者：
Cerioli, Andrea;Riani, Marco;Corbellini, Aldo
诊断工具必须依靠强大的高分解方法，以避免在存在离群值污染的情况下出现失真。然而，具有单一的、即使是稳健的数据汇总的缺点是，必须在分析之前做出关于稳健方法的参数的重要选择，例如故障点。这种选择的效果可能很难评估。我们认为，一个有效的解决方案是查看几张可用数据的图片，甚至可能是一部完整的电影。这可以通过在一系列参数值上监测通过选择的稳健方法计算的结果来实现。我们通过使用不同的高分解技术对多变量数据集进行分析，展示了监测在复杂数据结构研究中提供的信息增益。我们的发现支持这样一种说法，即监测的原则是非常灵活的，它可以导致尽可能有效的稳健估计。我们还通过模拟解决了监控中出现的一些棘手的推理问题。
Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in the presence of contamination by outliers. However, a disadvantage of having a single, even if robust, summary of the data is that important choices concerning parameters of the robust method, such as breakdown point, have to be made prior to the analysis. The effect of such choices may be difficult to evaluate. We argue that an effective solution is to look at several pictures, and possibly to a whole movie, of the available data. This can be achieved by monitoring, over a range of parameter values, the results computed through the robust methodology of choice. We show the information gain that monitoring provides in the study of complex data structures through the analysis of multivariate datasets using different high-breakdown techniques. Our findings support the claim that the principle of monitoring is very flexible and that it can lead to robust estimators that are as efficient as possible. We also address through simulation some of the tricky inferential issues that arise from monitoring.