An evaluation of diagnostic tests and their roles in validating forest biometric models

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Individual tree height increment model for managed even-aged stands of ponderosa pine throughout the western United States using linear mixed effects models (2006, background): “Therefore, it is important to know that model validation is not designed to prove that a model is correct (Popper, 1963), but rather to show that model predictions are close enough to independent data and that decisions made based on the model are defensible (Yang et al., 2004).”
Height-diameter models with random coefficients and site variables for tree species of Central Maine (2008, methodology): “[46] show that the numerous diagnostic tests used for model validation have limited use.”
A generalized height–diameter model with random coefficients for uneven-aged stands in El Salto, Durango (Mexico) (2009, methodology): “…as splitting the dataset in two parts, using one for fi tting and the other for validation (cross-validation), does not provide any information in addition to the respective statistics obtained directly from the model fi tted with the entire dataset ( Kozak and Kozak, 2003 ; Yang et al. , 2004 ).”
Forest Growth and Yield Modeling (2011, methodology): “When these statistical tests and several others were applied to nine datasets, Yang et al. (2004) found that their usefulness was limited, and cautioned against having them be the sole determinant of model performance.”