Steepest Ascent With Reset

Table Of Contents

Manual
- Getting Started
  - Starting the Program
  - Retrieving Data
  - Manipulating Data
  - Saving and Re-running Actions
    - Reviewing and accessing the code
    - Saving and re-running statistical models
- Sampling
- Procedures
  - Measurement Models
  - MML Models for Test Data
  - Other Available Procedures
- Graphics
- Tools
  - Estimation Methods
  - Optimization Techniques
  - Variance Estimation
- Post-hoc Procedures
- More user input instructions
  - The User Interface
    - Completed Run Queue
    - The Variable List
  - Input Instructions
  - Options
  - Output Precision
- Glossary of Terms and Symbols

Steepest Ascent With Reset - DRAFT VERSION

Overview

In situations where the steepest ascent algorithm fails to yield a significant improvement in the log likelihood function, the user has the option of specifying that the algorithm reverts to taking a Newton-Raphson step. This optimization method is known as the Steepest Ascent with reset.

References

Cohen, J. (1997). An Overview of AIR’s Direct Estimation Software for Marginal Maximum Likelihood Models. Washington, DC: American Institutes for Research.