The SEVERITY Procedure
- Overview
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Getting Started
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Syntax
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Details
Predefined Distributions Censoring and Truncation Parameter Estimation Method Parameter Initialization Estimating Regression Effects Empirical Distribution Function Estimation Methods Statistics of Fit Defining a Distribution Model with the FCMP Procedure Predefined Utility Functions Custom Objective Functions Input Data Sets Output Data Sets Displayed Output ODS Graphics
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Examples
Defining a Model for Gaussian Distribution Defining a Model for Gaussian Distribution with a Scale Parameter Defining a Model for Mixed-Tail Distributions Estimating Parameters Using Cramér-von Mises Estimator Fitting a Scaled Tweedie Model with Regressors Fitting Distributions to Interval-Censored Data
- References
| Parameter Initialization |
PROC SEVERITY enables you to initialize parameters of a model in different ways. There can be two kinds of parameters in a model: distribution parameters and regression parameters.
The distribution parameters can be initialized by using one of the following three methods:
- INIT= option
You can use the INIT= option in the DIST statement.
- INEST= data set
You can use the INEST= data set.
- PARMINIT subroutine
You can define a dist_PARMINIT subroutine in the distribution model. See the section Defining a Distribution Model with the FCMP Procedure for details.
Note that only one of the initialization methods is used. You cannot combine them. They are used in the following order:
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The method that uses the INIT= option takes the highest precedence. If you use the INIT= option to provide an initial value for at least one parameter, then other initialization methods (INEST= and PARMINIT) are not used. If you specify initial values for some but not all the parameters by using the INIT= option, then the uninitialized parameters are initialized to the default value of 0.001.
If this option is used when regression effects are specified, then the value of the first distribution parameter must be related to the initial value for the base value of the scale or log-transformed scale parameter. See the section Estimating Regression Effects for details.
The method that uses the INEST= data set takes the second precedence. If there is a nonmissing value specified for even one distribution parameter, then the PARMINIT method is not used and any uninitialized parameters are initialized to the default value of 0.001.
If none of the distribution parameters are initialized by using the INIT= option or the INEST= data set, but the distribution model defines a PARMINIT subroutine, then PROC SEVERITY invokes that subroutine with appropriate inputs to initialize the parameters. If the PARMINIT subroutine returns missing values for some parameters, then those parameters are initialized to the default value of 0.001.
If none of the initialization methods are used, each distribution parameter is initialized to the default value of 0.001.
See the section Estimating Regression Effects for details on regression models and initialization of regression parameters.