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Examples
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The SEVERITY Procedure
Overview
Getting Started
A Simple Example of Fitting Predefined Distributions
An Example with Left-Truncation and Right-Censoring
An Example of Modeling Regression Effects
Syntax
Functional Summary
PROC SEVERITY Statement
BY Statement
DIST Statement
LOSS Statement
NLOPTIONS Statement
OUTSCORELIB Statement
SCALEMODEL Statement
WEIGHT Statement
Programming Statements
Details
Predefined Distributions
Censoring and Truncation
Parameter Estimation Method
Parameter Initialization
Estimating Regression Effects
Empirical Distribution Function Estimation Methods
Statistics of Fit
Defining a Distribution Model with the FCMP Procedure
Predefined Utility Functions
Scoring Functions
Custom Objective Functions
Multithreaded Computation
Input Data Sets
Output Data Sets
Displayed Output
ODS Graphics
Examples
Defining a Model for Gaussian Distribution
Defining a Model for Gaussian Distribution with a Scale Parameter
Defining a Model for Mixed-Tail Distributions
Estimating Parameters Using Cramér-von Mises Estimator
Fitting a Scaled Tweedie Model with Regressors
Fitting Distributions to Interval-Censored Data
Defining a Finite Mixture Model That Has a Scale Parameter
Predicting Mean and Value-at-Risk by Using Scoring Functions
References
Examples: SEVERITY Procedure
Subsections:
23.1 Defining a Model for Gaussian Distribution
23.2 Defining a Model for 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
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