The VARCOMP Procedure
| Confidence Limits |
When no exact confidence limits exist, it is common practice to use approximate confidence limits. Two such approximations are the modified large-sample (MLS) method and the generalized confidence limit (GCL) method as discussed in Burdick, Borror, and Montgomery (2005). When analyzing a balanced one-way or two-way design, if you specify the CL= option with METHOD=TYPE1 or GRR, the VARCOMP procedure computes confidence limits by using either the MLS method (the default) or the GCL method. Generalized confidence limits are obtained by specifying the CL=GCL option in the MODEL statement.
MLS Confidence Limits
The method of MLS confidence limits was first introduced by Graybill and Wang (1980). It starts with approximate large-sample confidence limits; then it modifies the limits to be exact under certain parameter conditions.
For a balanced two-way crossed random model with interaction, formulas for the MLS method are given in Table 97.5. See Burdick, Borror, and Montgomery (2005) for the formulas for one-way or balanced two-way with no interaction models.
Confidence limits for parameters such as variances and their ratios might not contain the corresponding point estimates, because negative confidence bounds are increased to zero.
Parameter |
Lower Bound |
Upper Bound |
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)% MLS Confidence Limits
The terms in Table 97.5 are defined as follows:
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The symbol 
represents the percentile of an F distribution with df1 and df2 degrees of freedom and area 
to the left.
Generalized Confidence Limits
The method of generalized confidence limits was first introduced by Weerahandi (1993). The 100(1-)% generalized confidence limits are determined as follows:
Initialize the random number generator with the seed. The seed value is specified by the SEED= option.
Sample
generalized pivot quantities (GPQ), defined to have a distribution that is independent of the parameters under study. The value is specified by the NSAMPLE= option. Define the lower and upper limits as the
and 
quantiles of the sampled GPQ values.
Formulas for generalized confidence limits are given in Table 97.6, where Z denotes a standard normal random variable and 
and 
denote jointly independent chi-squared random variables that are independent of 
with degrees of freedom 
and 
, respectively. The value of 
in Table 97.6 is specified by the EPSILON= option.
Parameter |
GPQ |
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In general, the GCL method provides a more accurate confidence interval with a shorter interval width than the MLS method. However, the greater accuracy comes at the cost of being somewhat nondeterministic, because of the reliance on simulation.