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Title: Testing copula regression against benchmark models for point and interval estimation of tree wood volume in beech stands
Authors: Serinaldi, Francesco
Grimaldi, Salvatore
Abdolhosseini, Mohammad
Corona, Piermaria
Cimini, Dora
Keywords: Fagus sylvatica;Weighted regression;Box-Cox transformation;Copula regression;Normal quantile transformation;Uncertainty
Issue Date: 2012
Publisher: Springer-Verlag
Source: Serinaldi, F. et al. 2012. Testing copula regression against benchmark models for point and interval estimation of tree wood volume in beech stands. "European Journal of Forest Research" 131:1313–1326
This study compares copula regression, recently
introduced in the forest biometric literature, with four
benchmark regression models for computing wood volume
V in forest stands given the values of diameter at breast height
D and total height H, and suggests a set of statistical techniques
for the accurate assessment of model performance.
Two regression models deduced from the trivariate copulabased
distribution of V, D, and H are tested against the
classical Spurr’s model and Schumacher-Hall’s model based
on allometric and geometric concepts, and two regression
models that rely on Box-Cox transformed variables and are
in a middle ground, in terms of model complexity, between
copula-based regression and classical models. The accuracy
of the point estimates of V is assessed by a suitable set of
performance criteria and the nonparametric sign test,
whereas the associated uncertainty is evaluated by comparing
empirical and nominal coverage probabilities of the
prediction intervals. Focusing on point estimates, the
Schumacher-Hall’s model outperforms the other models in
terms of several performance criteria. The sign test points out
that the differences among the models that involve D and
H as separate covariates are not definitely significant,
whereas these models outperform the models with a single
covariate. As far as the interval estimates are of concern, the
four benchmark models provide comparable interval estimates.
The copula-based model with parametric marginals is
definitely outperformed by its competitors according to all
criteria, whereas the copula-based model with nonparametric
marginals provides quite accurate point estimates but
biased interval estimates of V.
L'articolo è disponibile sul sito dell'editore
ISSN: 1612-4669
DOI: 10.1007/s10342-012-0600-2
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