The HPSEVERITY Procedure
- Overview
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Getting Started
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Syntax
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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
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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 Functions
- References
WEIGHT variable-name ;
The WEIGHT statement specifies the name of a variable whose values represent the weight of each observation. PROC HPSEVERITY
associates a weight of 
to each observation, where is the value of the WEIGHT variable for the observation. If the weight value is missing or less than or equal to 0, then
the observation is ignored and a warning is written to the SAS log. When you do not specify the WEIGHT statement, each observation
is assigned a weight of 1. If you specify more than one WEIGHT statement, then the last statement is used.
The weights are normalized so that they add up to the actual sample size. In particular, the weight of each observation is
multiplied by 
, where 
is the sample size.