This section describes statistics that are computed for each observation when you fit a model for recurrent events data. For regression models that are fit using the MODEL statement, you can specify a variety of statistics to be computed for each observation in the input data set. This section describes the method of computation for each statistic. See Table 16.32 and Table 16.34 for the syntax to request these statistics.
Let be the event time in the ith observation in the input data set. The following statistics use the definitions of the mean function and intensity function in Table 16.72, where and are replaced by their maximum likelihood estimates. The shape parameter is assumed to be constant for all observations. For regression models, the scale parameter in Table 16.72 for the ith observation is

where are regression coefficients and are the maximum likelihood estimates of the regression parameters.
The scale parameter that is predicted by the model for the ith observation is

where is the vector of explanatory variables for the ith observation and is the vector of maximum likelihood estimates of the regression parameters.
The predicted mean function is computed as .
Confidence limits for the estimated are computed as described in the section Table 16.74, using , and .
The standard error of the estimated is computed as described in the section Table 16.74, using , and .
The predicted intensity function is computed as .
Confidence limits for the estimated are computed as described in the section Table 16.74, using , and .
The standard error of the estimated is computed as described in the section Table 16.74, using , and .