Spatial Assessment of Model Errors from Four Regression Techniques

By Lianjun Zhang, J. Gove. Forestry sciences, 2005

60 citations recorded by the source corpus.

Forest modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographically weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study area and across tree size classes. Little attention has been paid to the spatial heterogeneity of model performance. In this study, we used local Moran coefficients to investigate the spatial distributions of the model errors from the four regression models. The results indicated that GAM improved model-fitting to the data and provided better predictions for the response variable. However, it is nonspatial in nature and, consequently, generated spatial distributions for the model errors similar to the ones from ordinary least-squares (OLS). Furthermore, OLS and GAM yielded more clusters of similar (either positive or negative) model errors, indicating that trees in some subareas were either all underestimated or all overestimated for the response variable. In contrast, LMM was able to model the spatial covariance structures in the data and obtain more accurate predictions by accounting for the effects of spatial autocorrelations through the empirical best linear unbiased predictors. GWR is a varying-coefficient modeling technique. It estimated the model coefficients locally at each tree in the example plot and resulted in more accurate predictions for the response variable. Moreover, the spatial distributions of the model errors from LMM and GWR were more desirable, with fewer clusters of dissimilar model errors than the ones derived from OLS and GAM. FOR. SCI. 51(4):334–346.

Subfields: Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/24841c049c9db7160677c986416b1f0409e33926

How researchers see this paper

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In Economics and Econometrics

1. Luo Guo
2. Zhihai Ma
3. Lianjun Zhang

In Nature and Landscape Conservation

1. Luo Guo
2. Zhihai Ma
3. Lianjun Zhang
4. J. Gove
5. M. Mathur

Citations in the corpus

• Comparison of bandwidth selection in application of geographically weighted regression : a case study (2008, background): “The negative Ii values are represented by open circles, indicating the ‘‘cold spots’’ of the clusters with the opposite sign to the model residuals (Zhang and Gove 2005; Zhang et al. 2005).”