The ROBUSTREG Procedure

PROC ROBUSTREG implements a generalized MCD algorithm that is based on the fast-MCD algorithm formulated by Rousseeuw and Van Driessen (1999), which is similar to the algorithm for least trimmed squares (LTS).

The canonical Mahalanobis distance is defined as

where and are the empirical multivariate location and scatter, respectively. Here excludes the intercept. The relationship between the Mahalanobis distance and the hat matrix is

The canonical robust distance is defined as

where and are the robust multivariate location and scatter, respectively, that are obtained by the MCD method.

To achieve robustness, the MCD algorithm estimates the covariance of a multivariate data set mainly through an MCD h-point subset of the data set. This subset has the smallest sample-covariance determinant among all possible h-subsets. Accordingly, the breakdown value for the MCD algorithm equals . This means the MCD estimate is reliable, even if up to observations in the data set are contaminated.

(View the complete code for this example.)

It is possible that the original data are in p-dimensional space but the h-point subset that yields the minimum covariance determinant lies in a lower-dimensional hyperplane. Applying the canonical MCD algorithm to such a data set would result in a singular covariance problem (called exact fit in Rousseeuw and Van Driessen 1999), such that the relevant robust distances cannot be computed. To deal with the singularity problem and provide further leverage-point analysis, PROC ROBUSTREG implements a generalized MCD algorithm. (For more information, see the section Generalized MCD Algorithm.) The algorithm distinguishes in-(hyper)plane points from off-(hyper)plane points, and performs MCD leverage-point analysis in the dimension-reduced space by projecting all points onto the hyperplane.

Low-dimensional structure is often induced by classification covariates. Suppose that, in a study that has 25 female subjects and 5 male subjects, gender is the only classification effect. If the breakdown setting is greater than , the canonical MCD algorithm fails, and so does the relevant leverage-point analysis. In this case, the MCD h-subset would contain only female observations, and the constant gender in the h-subset would cause the relevant MCD estimate to be singular. The generalized MCD algorithm solves that problem by identifying all male observations as off-plane leverage points, and then carries out the leverage-point analysis, with all the other covariates being centered separately for female and male groups against their group means.

In general, low-dimensional structure is not necessarily due to classification covariates. Imagine that 80 children are supposed to play on a straight trail (denoted by ) but that some adventurous children go off the trail. The following statements generate the Children data set and the relevant scatter plot:

data Children;
   do i=1 to 80;
      off_trail=ranuni(321)>.9;
      x=rannor(111)*ranuni(321);
      trail_x=(i-40)/80*3;
      trail_y=trail_x;
      if off_trail=1 then y=x-1+rannor(321);
      else y=x;
      output;
   end;
run;

proc sgplot data=Children;
   series   x=trail_x y=trail_y/lineattrs=(color="red" pattern=4);
   scatter  x=x y=y/group=off_trail;
   ellipse  x=x y=y/alpha=.05 lineattrs=(color="green" pattern=34);
run;

Figure 17 shows the positions of all 80 children, the trail (as a red dashed line), and a contour curve of regular Mahalanobis distance that is centered at the mean position (as a green dotted ellipse). In terms of regular Mahalanobis distance, the associated covariance estimate is not singular, but its relevant leverage-point analysis completely ignores the trail (which is the entity of the low-dimensional structure). The children outside the ellipse are defined as leverage points, but the children off the trail would not be viewed as leverage points unless they had large Mahalanobis distances. As mentioned in Rousseeuw and Van Driessen (1999), the canonical MCD method can find the low-dimensional structure, but it does not provide further robust covariance estimation because the MCD covariance estimate is singular. As an improved version of the canonical MCD method, the generalized MCD method can find the trail, identify the children off the trail as off-plane leverage points, and further execute in-plane leverage analysis. The following statements apply the generalized MCD algorithm to the Children data set:

ods graphics on;
proc robustreg data=children plots=ddplot(label=none);
   model i = x y/leverage(mcdinfo opc);
run;
ods graphics off;

Figure 17: Scatter Plot for Children Data

Figure 18 exactly identifies the equation that underlies the trail. The analysis projects off-plane points onto the trail and computes their projected robust distances and projected Mahalanobis distances the same way as it does for the in-plane points.

Figure 19 shows the relevant distance-distance plot. Robust distance is typically greater than Mahalanobis distance because the sample covariance can be strongly influenced by unusual points that cause the sample covariance to be larger than the MCD covariance.

Figure 19: DD Plot for Children Data

Note: The PROC ROBUSTREG step in this example is used to obtain the leverage diagnostics; the response is not relevant for this analysis.

Through the off-plane and in-plane symbols and the horizontal cutoff line in Figure 19, you can separate the children into four groups:

The children in the latter three groups are defined as leverage points in PROC ROBUSTREG.

The generalized MCD algorithm follows the same resampling strategy as the canonical MCD algorithm by Rousseeuw and Van Driessen (1999) but with the following modifications:

If the data set under investigation has a low-dimensional structure, you can use two ODS objects, DependenceEquations and MCDDependenceEquations, to identify the regressors that are linear combinations of other regressors plus certain constants. The equations in DependenceEquations hold for the entire data set, whereas the equations in MCDDependenceEquations apply only to the majority of the observations.

By using the OPC suboption of the LEVERAGE option, you can request an ODS table called DroppedComponents. Figure 20 shows the DroppedComponents table for the Children data set example. This table contains a set of coefficient vectors for regressors, which form a basis of the complementary space for the relevant low-dimensional structure.

By using the MCDINFO suboption of the LEVERAGE option, you can request that detailed information about the MCD covariance estimate be displayed in four ODS tables: MCDProfile, MCDCenter, MCDCov, and MCDCorr. Figure 21 shows an example of the MCD information tables for the Children data set. The number of dimensions in the table MCDProfile equals the number of nonintercept regressors minus the number of design-dropped components. The specified value of H is the same as h for the h-subset that you can specify by using the QUANTILE= suboption of the LEVERAGE option in the MODEL statement. The reweighted H is the number of observations that are actually used to compute the MCD center and MCD covariance after the reweighting step of the MCD algorithm.