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Computationally, Q is about the same order as MDFFIT and considerably more complicated than A. However, Andrews and Pregibon have succeeded in developing a distribution theory for Q when y is Gaussian and X is fixed. While useful only for n of modest size, it does provide some significance levels for finding sets of outliers. Both A and Q are computationally feasible for m<20. A stepwise approach based on the Mahalanobis distance and the ideas of robust covariance [Devlin, Gnanadesikan, and Kettenring (i975)] can be used for larger subsets.
28. One DFBETAS for Libya (49) is large in an absolute sense as well. Note that the deletion of only one data point out of 50 is causing more than one standard error of change in an estimated coefficient. 6398. 6561. 1008 0. 3389. 3797. 2954. 0244. 28. Cmariance Matrix Sensitiuiw. 7 presents the COVRATIOs for the intercountry life-cycle savings data. 36) that COVRATIO is a ratio of the determinant of the estimated coefficient covariance matrix with the ith observation deleted to that of the estimated covariance matrix based on the full data set.
Explanatory variables) and influential points at the same time. Thus one must first choose a set of explanatory variables and stay with them while the dummy variables are selected. Of course, this process may be iterated and, if some observations are deleted, a new stepwise regression on the explanatory variable set should be performed. Stepwise regression also clearly fails to consider all possible combinations of the dummy variables and can therefore m i s s influential points when more than one is present.