Modeling and Prediction of Forest Growth Variables Based on Multilevel Nonlinear Mixed Models

摘要

In this article, we describe estimation and prediction methods for nonlinear modeling of forest growth variables that are subject to nested sources of variability. The multilevel nonlinear mixed - effects models that we consider are useful for a variety of forestry applications, but we concentrae on the problem of estimating, and making projections from, growth curves for tree height based on logitudinal data grouped by location. Wolfinger and Lin consider estimating equation approaches to fitting more general nonlinear mixed-effects models, and we adapt their zero-expansion estimating equations to the multilevel case.