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The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region. This process is continued until no outliers remain in a data set.
An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. [ 2 ] Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data.
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection [4] discusses the general pattern in various local outlier detection methods (including, e.g., LOF, a simplified version of LOF and LoOP) and abstracts from this into a general framework. This framework is then ...
The book has seven chapters. [1] [4] The first is introductory; it describes simple linear regression (in which there is only one independent variable), discusses the possibility of outliers that corrupt either the dependent or the independent variable, provides examples in which outliers produce misleading results, defines the breakdown point, and briefly introduces several methods for robust ...
The M in M-estimation stands for "maximum likelihood type". The method is robust to outliers in the response variable, but turned out not to be resistant to outliers in the explanatory variables (leverage points). In fact, when there are outliers in the explanatory variables, the method has no advantage over least squares.
Graphical procedures such as plots are a short path to gaining insight into a data set in terms of testing assumptions, model selection, model validation, estimator selection, relationship identification, factor effect determination, outlier detection. Statistical graphics give insight into aspects of the underlying structure of the data. [1]
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The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables. [8] Multivariate normality: Independent variables are normal for each level of the grouping variable. [10] [8]