Statistical Methods & Applications

, Volume 27, Issue 4, pp 625–629 | Cite as

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”

  • Simon J. SheatherEmail author
  • Joseph W. McKean
Original Article


In this discussion, we consider two examples. The first example concerns the Old Faithful data, which the authors (Cerioli, Riani, Atkinson, Corbellini in Stat Methods Appl, to appear) discuss in detail in their paper. The second example, which is taken from, is based on the prices and other attributes of 53,900 diamonds. The point of our discussion is to demonstrate that the process of producing valid models and then looking at diagnostics, that compare least squares and robust fits, can also effectively identify outliers and/or important structure missing from the model. Using this approach, we identify a dramatic change point in the diamonds data. We are very curious about what information the sophisticated monitoring methods produce about this change point and its effects on the outcome variable.


High breakdown Rank-based Robust Robust diagnostics Wilcoxon 



We would like to thank an anonymous referee for his suggestions on clarification of several passages of the paper.


  1. Abebe A, McKean JW, Kloke JD, Bilgic YK (2016) Iterated reweighted rank-based estimates for GEE models. In: Liu R, McKean JW (eds) Robust rank-based and nonparametric methods. Springer, Berlin, pp 61–79CrossRefGoogle Scholar
  2. Cerioli A, Riani M, Atkinson AC, Corbellini A The power of monitoring: How to make the most of a contaminated multivariate sample. Stat Methods Appl (to appear)Google Scholar
  3. Kloke JD, McKean JW (2014) Nonparametric statistical methods using R. Chapman-Hall, Boca RatonCrossRefGoogle Scholar
  4. McKean JW, Hettmansperger T (2016) Rank-based analysis of linear models and beyond: a review. In: Liu R, McKean JW (eds) Robust rank-based and nonparametric methods. Springer, Berlin, pp 1–24Google Scholar
  5. McKean JW, Naranjo JD, Sheather SJ (1996) Diagnostics to detect differences in robust fits of linear models. Comput Stat 11:223–243MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsTexas A&M UniversityCollege StationUSA
  2. 2.Department of StatisticsWestern Michigan UniversityKalamazooUSA

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