An object-oriented database programming environment for by Supcik J.

By Supcik J.

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76) This is only approximate, since we are interested in extreme values. It would be even better to know the distribution of A conditional on X, but 38 DETECTING INFLUENTIAL OBSERVATIONS AND OUTLIERS this remains an open problem. More than just the smallest value of A should be examined for each m, since there may be several significant groups. Gaps in the values of A are also usually worth noting. Andrews and Pregibon (1978) have proposed another method based on Z. 51) for m= 1. If Z instead of Z had been used, Q becomes another form of Wilks' A statistic where there are m + 1 groups: one for each row in Dm and one group for all the remaining rows.

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.

Diflerentirtion. We examine now a second means for identifying influential observations, differentiation of regression outputs with respect to specific model parameters. In particular, we can alter the weight attached to the ith observation if, in the assumptions of the standard linear regression model, we replace var(q) = u2 with var(ci)= a2/wi, for the specific i only. Differentiation of the regression coefficients with respect to w,, evaluated at wi = 1, provides a means for examining the sensitivity of the regression coefficients to a slight change in the weight given to the ith observation.

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