Using Numerical Optimization for Specifying Individual-Tree Competition Models

摘要

In this article we present a method that combines maximum likelihood estimation and nonlinear programming in growth modeling. The method of hooke and Jeeves is used to discover the optimal specification of a particular competition index type, while statistical software is used to fit the regression model with the given competition algorithm, which alters the specification of the competition software is fed back to the optimization algorithm, which alters the specification of the competition index type based on the changes in the log-likelihood. index type based on the changes in the log-likelihood