## Negative Variance Component Estimates

The variance components estimated by PROC VARCOMP should
theoretically be nonnegative because they are assumed to represent the
variance of a random variable. Nevertheless, when you are using
METHOD=MIVQUE0 (the default) or METHOD=TYPE1, some estimates of
variance components may become negative.
(Due to the nature of the algorithms used for METHOD=ML and
METHOD=REML, negative estimates are constrained to zero.)
These negative estimates may arise for a variety of reasons:
- The variability in your data may be large enough
to produce a negative estimate, even though the
true value of the variance component is positive.
- Your data may contain outliers.
Refer to Hocking (1983) for a graphical technique for detecting
outliers in variance components models using the SAS System.
- A different model for interpreting
your data may be appropriate.
Under some statistical models for variance components
analysis, negative estimates are an indication that
observations in your data are negatively correlated.
Refer to Hocking (1984) for further information about these models.

Assuming that you are satisfied that the model PROC VARCOMP is using
is appropriate for your data, it is common practice to treat negative
variance components as if they are zero.

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.