The SEVERITY Procedure
- Overview
-
Getting Started
-
Syntax
-
DetailsPredefined DistributionsCensoring and TruncationParameter Estimation MethodParameter InitializationEstimating Regression EffectsLevelization of Classification VariablesSpecification and Parameterization of Model EffectsEmpirical Distribution Function Estimation MethodsStatistics of FitDefining a Severity Distribution Model with the FCMP ProcedurePredefined Utility FunctionsScoring FunctionsCustom Objective FunctionsMultithreaded ComputationInput Data SetsOutput Data SetsDisplayed OutputODS Graphics
-
ExamplesDefining a Model for Gaussian DistributionDefining a Model for the Gaussian Distribution with a Scale ParameterDefining a Model for Mixed-Tail DistributionsEstimating Parameters Using Cramér-von Mises EstimatorFitting a Scaled Tweedie Model with RegressorsFitting Distributions to Interval-Censored DataDefining a Finite Mixture Model That Has a Scale ParameterPredicting Mean and Value-at-Risk by Using Scoring FunctionsScale Regression with Rich Regression Effects
- References
Subsections:
- 23.1 Defining a Model for Gaussian Distribution
- 23.2 Defining a Model for the Gaussian Distribution with a Scale Parameter
- 23.3 Defining a Model for Mixed-Tail Distributions
- 23.4 Estimating Parameters Using Cramér-von Mises Estimator
- 23.5 Fitting a Scaled Tweedie Model with Regressors
- 23.6 Fitting Distributions to Interval-Censored Data
- 23.7 Defining a Finite Mixture Model That Has a Scale Parameter
- 23.8 Predicting Mean and Value-at-Risk by Using Scoring Functions
- 23.9 Scale Regression with Rich Regression Effects