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

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Abstract

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 www.kaggle.com, 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.

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References

  • 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 61–79

    Chapter  Google Scholar 

  • 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)

  • Kloke JD, McKean JW (2014) Nonparametric statistical methods using R. Chapman-Hall, Boca Raton

    Book  Google Scholar 

  • 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 1–24

    Google Scholar 

  • McKean JW, Naranjo JD, Sheather SJ (1996) Diagnostics to detect differences in robust fits of linear models. Comput Stat 11:223–243

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Statistics, Texas A&M University, College Station, TX, USA

    Simon J. Sheather

  2. Department of Statistics, Western Michigan University, Kalamazoo, MI, USA

    Joseph W. McKean

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  1. Simon J. Sheather
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  2. Joseph W. McKean
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Correspondence to Simon J. Sheather.

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Sheather, S.J., McKean, J.W. Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”. Stat Methods Appl 27, 625–629 (2018). https://doi.org/10.1007/s10260-018-0422-6

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  • DOI: https://doi.org/10.1007/s10260-018-0422-6

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