The HPSEVERITY Procedure
- Overview
-
Getting Started
-
Syntax
-
DetailsPredefined DistributionsCensoring and TruncationParameter Estimation MethodDetails of Optimization AlgorithmsParameter InitializationEstimating Regression 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 Output
-
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 Functions
- References
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