Shared Concepts
Backward Elimination
This section applies to the following procedures: CNTSELECT and SEVSELECT.
METHOD=BACKWARD specifies the backward elimination technique. This technique starts from the full model, which includes all independent effects. Then effects are deleted one by one until a stopping condition is satisfied. At each step, the effect that shows the smallest contribution to the model is deleted.
Just as with forward selection, you can use the SELECT= option to change the criterion that is used to assess effect contributions. You can also specify a stopping criterion in the STOP= option and use a CHOOSE= option to provide a criterion for selecting among the sequence of models produced. For more information, see the discussion in the section Forward Selection.
Examples of Backward Selection Specifications
The following statement removes effects that at each step produce the largest value of the Schwarz Bayesian information criterion (SBC) statistic and stops at the step where removing any effect increases the SBC statistic:
selection method=backward stophorizon=1;
The following statement bases removal of effects on the corrected Akaike’s information criterion (AICC) and stops at the first step whose AICC value is smaller than the AICC value at the next three steps:
selection method=backward(select=AICC);