The SEVERITY Procedure
- Overview
-
Getting Started
-
Syntax
-
DetailsPredefined DistributionsCensoring and TruncationParameter Estimation MethodParameter InitializationEstimating Regression EffectsEmpirical Distribution Function Estimation MethodsStatistics of FitDefining a Distribution Model with the FCMP ProcedurePredefined Utility FunctionsCustom Objective FunctionsMultithreaded ComputationInput Data SetsOutput Data SetsDisplayed OutputODS Graphics
-
Examples
- References
- AllFitStatistics (PRINT=ALLFITSTATS)
- ConvergenceStatus (PRINT=CONVSTATUS)
- DescStats (PRINT=DESCSTATS)
- DistributionInfo (PRINT=DISTINFO)
- InitialValues (PRINT=INITIALVALUES)
- IterationHistory (PRINT=NLOHISTORY)
- ModelSelection (PRINT=SELECTION)
- OptimizationSummary (PRINT=NLOSUMMARY)
- ParameterEstimates (PRINT=ESTIMATES)
- RegDescStats (PRINT=DESCSTATS)
- StatisticsOfFit (PRINT=STATISTICS)
- TurnbullSummary (PRINT=ALL)
The SEVERITY procedure optionally produces displayed output by using the Output Delivery System (ODS). All output is controlled by the PRINT= option in the PROC SEVERITY statement. Table 23.5 relates the PRINT= options to ODS tables.
Table 23.5: ODS Tables Produced in PROC SEVERITY
|
ODS Table Name |
Description |
Option |
|---|---|---|
|
AllFitStatistics |
Statistics of fit for all the distribution models |
PRINT=ALLFITSTATS |
|
ConvergenceStatus |
Convergence status of parameter estimation process |
PRINT=CONVSTATUS |
|
DescStats |
Descriptive statistics for the response variable |
PRINT=DESCSTATS |
|
DistributionInfo |
Distribution information |
PRINT=DISTINFO |
|
InitialValues |
Initial parameter values and bounds |
PRINT=INITIALVALUES |
|
IterationHistory |
Optimization iteration history |
PRINT=NLOHISTORY |
|
ModelSelection |
Model selection summary |
PRINT=SELECTION |
|
OptimizationSummary |
Optimization summary |
PRINT=NLOSUMMARY |
|
ParameterEstimates |
Final parameter estimates |
PRINT=ESTIMATES |
|
RegDescStats |
Descriptive statistics for the regressor variables |
PRINT=DESCSTATS |
|
StatisticsOfFit |
Statistics of fit |
PRINT=STATISTICS |
|
TurnbullSummary |
Turnbull EDF estimation summary |
PRINT=ALL |
If PRINT= option is not specified, then by default PROC SEVERITY produces ModelSelection, ConvergenceStatus, OptimizationSummary, StatisticsOfFit, and ParameterEstimates ODS tables.
The TurnbullSummary table is produced only if you specify PRINT=ALL and Turnbull’s method is used for computing EDF estimates.
displays the comparison of all the statistics of fit for all the models in one table. The table does not include the models whose parameter estimation process does not converge. If all the models fail to converge, then this table is not produced. If the table contains more than one model, then the best model according to each statistic is indicated with an asterisk (*) in that statistic’s column.
displays the convergence status of the parameter estimation process.
displays the information about all the candidate distribution. It includes the name, the description, the number of distribution parameters, and whether the distribution is valid for the specified modeling task.
displays the initial values and bounds used for estimating each model.
displays the iteration history of the nonlinear optimization process used for estimating the parameters.
displays the model selection table. The table shows the convergence status of each candidate model, and the value of the selection criterion along with an indication of the selected model.
displays the summary of the nonlinear optimization process used for estimating the parameters.
displays the final estimates of parameters. The estimates are not displayed for models whose parameter estimation process does not converge.
displays the descriptive statistics for the regressor variables, if you have specified the SCALEMODEL statement.
displays the statistics of fit for each model. The statistics of fit are not displayed for models whose parameter estimation process does not converge.
displays the summary of Turnbull’s estimation process if Turnbull’s method is used for computing EDF estimates. The summary includes whether the nonlinear optimization converged, the number of iterations, the maximum absolute relative error, the maximum absolute reduced gradient, and whether the final estimates are maximum likelihood estimates.