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, 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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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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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, 2022As 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 Statistical Pixel Estimation
Computer Graphics Forum, 2015AbstractRobust statistical methods are employed to reduce the noise in Monte Carlo ray tracing. Through the use of resampling, the sample mean distribution is determined for each pixel. Because this distribution is uni‐modal and normal for a large sample size, robust estimates converge to the true mean of the pixel values. Compared to existing methods,
Jin Woo Jung +2 more
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Robust statistics in shape fitting
Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003The concept of robustness in statistics is examined. Starting from the concepts of the breakdown point and equivariance properties of an estimator, the desired equivariance properties for shape fitting are defined, and high breakdown point methods with these properties are found. >
Andrew Stein, Michael Werman
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Robust statistics for outlier detection
WIREs Data Mining and Knowledge Discovery, 2011AbstractWhen 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, 1990Vijay K. Rohatgi +2 more
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Robustness of the Studentized Range Statistic
Biometrika, 1974SUMMARY 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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Robustness of Statistical Methods and Nonparametric Statistics.
Journal of the Royal Statistical Society. Series A (General), 1987Eric Ziegel, D. Rasch, M. Tiku
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