Improved Calibration of Nonlinear Mixed-Effects Models Demonstrated on a Height Growth Function
By S. Meng, S. Huang. Forestry sciences, 2009
96 citations recorded by the source corpus.
The calibration of a nonlinear mixed-effects model is of critical importance in making local predictions. In previous applications, simplified equations were generally used to calibrate nonlinear mixed-effects models fitted using the first-order methods implemented through the NLINMIX macro in SAS. This simplification, however, could distort local predictions. In this study, using a nonlinear height growth model of lodgepole pine (Pinus contorta var. latifolia Engelm.), we demonstrated on two data sets the procedures to obtain an improved calibration of the height growth model. The differences between the improved and conventional calibrations were found to be significant. Calibration of the nonlinear mixed models using the improved method resulted in not only reduced bias but also reduced variance of the errors. It is recommended that the improved calibration method be used. A computing program detailing the procedures for obtaining the improved calibration was developed.
Subfields: Atmospheric Science, Computational Mechanics, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/ad3e4da3f409f7ea516e836a64262244335b3360
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Citations in the corpus
- Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey (2018, methodology): “When a subsample of trees in plot i are measured, the random parameters ui for that plot can be computed from the first-order Taylor series expansion (Meng and Huang, 2009):”
- Calibrating fixed- and mixed-effects taper equations (2011, unknown): “…the taper equation The random parameters ui for tree i can be computed by use of the first-order Taylor series expansion (Fitzmaurice et al., 2004; Meng and Huang, 2009): ûkþ1i ¼ D̂Z T i ðZiD̂Z T i þ R̂iÞ 1½yi fðb̂; ûki ;xiÞ ð8Þ where ûki = estimate of the random parameters at the kth…”
- Prediction of tree diameter growth using quantile regression and mixed-effects models (2014, methodology): “The random parameters ui for tree i can be computed by use of the first-order Taylor series expansion (Meng and Huang, 2009):”
- Estimating and predicting bark thickness for seven conifer species in the Acadian Region of North America using a mixed-effects modeling approach: comparison of model forms and subsampling strategies (2011, methodology): “Recent applications of this technique were presented in Wang et al. (2007) and Meng and Huang (2009), where the focus was mostly placed on height–age modeling, and in Yang et al. (2009a, b) on stem profile modeling.”