The SEVERITY 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 EffectsEmpirical Distribution Function Estimation MethodsStatistics of FitDefining a Distribution Model with the FCMP ProcedurePredefined Utility FunctionsScoring FunctionsCustom Objective FunctionsMultithreaded ComputationInput Data SetsOutput Data SetsDisplayed OutputODS Graphics
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ExamplesDefining a Model for Gaussian DistributionDefining a Model for 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 Functions
- References
WEIGHT variable-name ;
The WEIGHT statement specifies the name of a variable whose values represent the weight of each observation. PROC SEVERITY
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, weight of each observation is multiplied
by 
, where 
is the sample size.