References
Agostinelli C, Greco L (2018) Weighted likelihood estimation of multivariate location and scatter. Test. https://doi.org/10.1007/s11749-018-0596-0
Andrews DF, Bickel PJ, Hampel FR, Tukey WJ, Huber PJ (1972) Robust estimates of location: survey and advances. Princeton University Press, Princeton
Atkinson AC (1973) Testing transformations to normality. J R Stat Soc Ser B 35:473–479
Box GEP (1953) Non-normality and tests on variances. Biometrika 40:318–335
Box GEP, Cox DR (1964) An analysis of transformations (with discussion). J R Stat Soc Ser B 26:211–246
Cerioli A, Riani M (2003) Robust methods for the analysis of spatially autocorrelated data. Stat Methods Appl 11:335–358
Cerioli A, Atkinson AC, Riani M (2016) How to marry robustness and applied statistics. In: Di Battista T, Moreno E, Racugno W (eds) Topics on methodological and applied statistical inference. Springer, Heidelberg, pp 51–64
Cerioli A, Farcomeni A, Riani M (2018) Wild adaptive trimming for robust estimation and cluster analysis. Scand J Stat. https://doi.org/10.1111/sjos.12349
Dotto F, Farcomeni A, García-Escudero LA, Mayo-Iscar A (2018) A reweighting approach to robust clustering. Stat Comput 28:477–493
Filzmoser P, Ruiz-Gazen A, Thomas-Agnan C (2014) Identification of local multivariate outliers. Stat Pap 55:29–47
Riani M, Atkinson AC (2000) Robust diagnostic data analysis: transformations in regression (with discussion). Technometrics 42:384–398
Riani M, Atkinson AC (2010) Robust model selection with flexible trimming. Comput Stat Data Anal 54:3300–3312
Riani M, Cerioli A, Atkinson AC, Perrotta D (2014) Monitoring robust regression. Electron J Stat 8:646–677
Riani M, Atkinson AC, Cerioli A, Corbellini A (2018) Robust methods via monitoring for clustering and multivariate data analysis. Submitted
Rousseeuw PJ, Van Den Bossche W (2018) Detecting deviating data cells. Technometrics 60:135–145
Acknowledgements
The work has been partially supported by the European Commission’s Hercule III programme 2014–2020 through the Automated Monitoring Tool project. This research benefits from the HPC (High Performance Computing) facility of the University of Parma, Italy. M.R. gratefully acknowledges support from the CRoNoS project, reference CRoNoS COST Action IC1408.
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Cerioli, A., Riani, M., Atkinson, A.C. et al. Rejoinder to the discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”. Stat Methods Appl 27, 661–666 (2018). https://doi.org/10.1007/s10260-018-00436-8
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DOI: https://doi.org/10.1007/s10260-018-00436-8