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Tutorial to robust statistics
Author(s) -
Rousseeuw Peter J.
Publication year - 1991
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180050103
Subject(s) - outlier , robust statistics , univariate , robust regression , statistics , multivariate statistics , estimator , sample (material) , computer science , robustness (evolution) , mathematics , regression , sample size determination , data mining , artificial intelligence , biochemistry , chemistry , chromatography , gene
In this tutorial we first illustrate the effect of outliers on classical statistics such as the sample average. This motivates the use of robust techniques. For univariate data the sample median is a robust estimator of location, and the dispersion can also be estimated robustly. The resulting ‘ z ‐scores’ are well suited to detect outliers. The sample median can be generalized to very large data sets, which is useful for robust ‘averaging’ of curves or images. For multivariate data a robust regression procedure is described. Its standardized residuals allow us to identify the outliers. Finally, a survey of related approaches is given. (This review overlaps with earlier work by the same author, which appeared elsewhere.)