Effects of Correlation among Parameters on Prediction Quality of a Process-Based Forest Growth Model

摘要

A nonparametric method was introduced as a technique for evaluating the effects of parameter correlation on prediction quality of process-based forest growth models. The method was based on a rank correlation and Cholesky decomposition. For a given data matrix, by reordering the observations, the method would produce a rearranged matrix with the desired correlation structure. The method was computationally simple and efficient.