Artificial Data Set generated by Hawkins, Bradu, and Kass (1984). The data set consists of 75 observations in four dimensions (one response and three explanatory variables). It provides a good example of the masking effect. The first 14 observations are outliers, created in two groups: 1–10 and 11–14. Only observations 12, 13 and 14 appear as outliers when using classical methods, but can be easily unmasked using robust distances computed by, e.g., MCD - covMcd().
A data frame with 75 observations on 4 variables, where the last variable is the dependent one.
This data set is also available in package wle as
Hawkins, D.M., Bradu, D., and Kass, G.V. (1984) Location of several outliers in multiple regression data using elemental sets. Technometrics 26, 197–208.
P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection; Wiley, p.94.
data(hbk) plot(hbk) summary(lm.hbk <- lm(Y ~ ., data = hbk)) hbk.x <- data.matrix(hbk[, 1:3]) (cHBK <- covMcd(hbk.x))