The HPSEVERITY Procedure
- Overview
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Getting Started
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Syntax
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DetailsPredefined DistributionsCensoring and TruncationParameter Estimation MethodParameter InitializationEstimating Regression EffectsLevelization of Classification VariablesSpecification and Parameterization of Model EffectsEmpirical Distribution Function Estimation MethodsStatistics of FitDistributed and Multithreaded ComputationDefining a Severity Distribution Model with the FCMP ProcedurePredefined Utility FunctionsScoring FunctionsCustom Objective FunctionsInput Data SetsOutput Data SetsDisplayed OutputODS Graphics
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ExamplesDefining a Model for Gaussian DistributionDefining a Model for the Gaussian Distribution with a Scale ParameterDefining a Model for Mixed-Tail DistributionsFitting a Scaled Tweedie Model with RegressorsFitting Distributions to Interval-Censored DataBenefits of Distributed and Multithreaded ComputingEstimating Parameters Using Cramér-von Mises EstimatorDefining a Finite Mixture Model That Has a Scale ParameterPredicting Mean and Value-at-Risk by Using Scoring FunctionsScale Regression with Rich Regression Effects
- References
Details: HPSEVERITY Procedure
Subsections:
- Predefined Distributions
- Censoring and Truncation
- Parameter Estimation Method
- Parameter Initialization
- Estimating Regression Effects
- Levelization of Classification Variables
- Specification and Parameterization of Model Effects
- Empirical Distribution Function Estimation Methods
- Statistics of Fit
- Distributed and Multithreaded Computation
- Defining a Severity Distribution Model with the FCMP Procedure
- Predefined Utility Functions
- Scoring Functions
- Custom Objective Functions
- Input Data Sets
- Output Data Sets
- Displayed Output
- ODS Graphics