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Robust Statistics [PDF]

, 2011
The first example involves the real data given in Table 1 which are the results of an interlaboratory test. The boxplots are shown in Fig. 1 where the dotted line denotes the mean of the observations and the solid line the median. We note that only the results of the Laboratories 1 and 3 lie below the mean whereas all the remaining laboratories return ...
Gather, Ursula, Davies, P. Laurie
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Robust Statistics

2018
In lieu of an abstract, here is the entry's first paragraph: Robust statistics are procedures that maintain nominal Type I error rates and statistical power in the presence of violations of the assumptions that underpin parametric inferential statistics.
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Robust statistical inference for matched win statistics

Statistical Methods in Medical Research, 2022
As alternatives to the time-to-first-event analysis of composite endpoints, the win statistics, that is, the net benefit, the win ratio, and the win odds have been proposed to assess treatment effects, using a hierarchy of prioritized component outcomes based on clinical relevance or severity. Whether we are using paired organs of a human body or pair-
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Robust statistics for outlier detection

WIREs Data Mining and Knowledge Discovery, 2011
AbstractWhen analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools.
Peter J. Rousseeuw, Mia Hubert
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Robustness of Statistical Tests.

Journal of the American Statistical Association, 1990
Vijay K. Rohatgi, Takeaki Kariya, Bimal K. Sinha   +2 more
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Robustness of Statistical Methods and Nonparametric Statistics.

Journal of the Royal Statistical Society. Series A (General), 1987
Eric Ziegel, D. Rasch, M. Tiku
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Robustness of the Studentized Range Statistic

Biometrika, 1974
SUMMARY The effects of heterogeneity of variance and nonnormality of population distribution on the sampling distribution of the studentized range are investigated.
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Robust Statistics.

Journal of the American Statistical Association, 1983
Leone Y. Low, Peter J. Huber
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Robust statistical deformable models

2017
During the last few years, we have witnessed tremendous advances in the field of 2D Deformable Models for the problem of landmark localization. These advances, which are mainly reported on the task of face alignment, have created two major and opposing families of methodologies. On the one hand, there are the generative Deformable Models that utilize a
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Robust Statistics

Technometrics, 1982
Stephen Portnoy, Peter J. Huber
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