The NLMIXED Procedure
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Overview
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Getting Started
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Syntax
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DetailsModeling Assumptions and NotationIntegral ApproximationsBuilt-in Log-Likelihood FunctionsHierarchical Model SpecificationOptimization AlgorithmsFinite-Difference Approximations of DerivativesHessian ScalingActive Set MethodsLine-Search MethodsRestricting the Step LengthComputational ProblemsCovariance MatrixPredictionComputational ResourcesDisplayed OutputODS Table Names
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Examples
- References
Subsections:
- Modeling Assumptions and Notation
- Integral Approximations
- Built-in Log-Likelihood Functions
- Hierarchical Model Specification
- Optimization Algorithms
- Finite-Difference Approximations of Derivatives
- Hessian Scaling
- Active Set Methods
- Line-Search Methods
- Restricting the Step Length
- Computational Problems
- Covariance Matrix
- Prediction
- Computational Resources
- Displayed Output
- ODS Table Names
This section contains details about the underlying theory and computations of PROC NLMIXED.