Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normally distributed. Robust statistical methods have been developed for many common problems, such as estimating location, scale and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from parametric distributions. For example, robust methods work well for mixtures of two normal distributions with different standard-deviations, for example, one and three; under this model, non-robust methods like a t-test work badly.
Read more about Robust Statistics: Introduction, Examples, Definition, Example: Speed of Light Data, Measures of Robustness, M-estimators, Robust Parametric Approaches, Related Concepts, Replacing Outliers and Missing Values, See Also
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