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Exact Maximum Likelihood Estimation (MLE)
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The COPULA Procedure (Experimental)

  • Overview
  • Getting Started
  • Syntax Procedure Syntax
    Functional Summary PROC COPULA Statement BOUNDS Statement BY Statement DEFINE Statement FIT Statement SIMULATE Statement VAR Statement
  • Details Procedure Details
    Sklar’s Theorem Dependence Measures Normal Copula Student’s t copula Archimedean Copulas Canonical Maximum Likelihood Estimation (CMLE) Exact Maximum Likelihood Estimation (MLE) Calibration Estimation Nonlinear Optimization Options Displayed Output OUTCOPULA= Data Set OUTPSEUDO=, OUT=, and OUTUNIFORM= Data Sets ODS Table Names ODS Graph Names
  • Examples Procedure Examples
    Copula Based VaR Estimation Simulating Default Times
  • References
 
Exact Maximum Likelihood Estimation (MLE)

Suppose that the marginal distributions of vector elements , are already known to be uniform. Then the parameter is estimated by exact maximum likelihood:

     
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