Finding Regressional Outliers by Dynamic Projections
نویسنده
چکیده
Atypical observations hidden in the data may play quite an disastrous role in a tted regression, especially when commonly used outlier detection techniques like computing leverages, Mahalanobis distances, ordinary and studentized residuals, DFFits, cross-validations { do not detect them. However (multivariate) outliers can be detected quite easily by graphical techniques , e.g. scatterplot matrices, spin plots or dynamic projections using the grand tour method. We nd here specially useful the grand tour. When we know about the outliers and their location in the multivariate space, then we may account for them by special regression models. We illustrate our considerations using the Modiied Wood Gravity data from Rousseeuw and Leroy (1987).
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تاریخ انتشار 1998