A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models

Key Points

• To establish a general computational procedure for obtaining maximum likelihood estimates across a wide range of generalized regression models.
• Formulated a unified mathematical and algorithmic framework to evaluate and maximize likelihood functions in generalized regression contexts.
• Provides a standardized estimation technique capable of solving complex generalized regression equations across diverse distribution families.
• Improves computational consistency and parameter estimation efficiency for advanced statistical modeling.