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
Examples: HPSEVERITY Procedure
Subsections:
- 9.1 Defining a Model for Gaussian Distribution
- 9.2 Defining a Model for the Gaussian Distribution with a Scale Parameter
- 9.3 Defining a Model for Mixed-Tail Distributions
- 9.4 Fitting a Scaled Tweedie Model with Regressors
- 9.5 Fitting Distributions to Interval-Censored Data
- 9.6 Benefits of Distributed and Multithreaded Computing
- 9.7 Estimating Parameters Using Cramér-von Mises Estimator
- 9.8 Defining a Finite Mixture Model That Has a Scale Parameter
- 9.9 Predicting Mean and Value-at-Risk by Using Scoring Functions
- 9.10 Scale Regression with Rich Regression Effects