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首页> 外文期刊>Journal of Forest Planning >Developing Forest Models from Longitudinal Data: A Case Study Assessing the Need to Account for Correlated and/or Heterogeneous Error Structures under a Nonlinear Mixed Model Framework
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Developing Forest Models from Longitudinal Data: A Case Study Assessing the Need to Account for Correlated and/or Heterogeneous Error Structures under a Nonlinear Mixed Model Framework

机译：从纵向数据开发森林模型：在非线性混合模型框架下评估需要考虑相关和/或异构错误结构的案例研究

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摘要

In this study we demonstrated the procedures for model estimation and prediction based on the nonlinear mixed model (NLMM) technique. Since the unequally spaced and unbalanced longitudinal data used to fit forest models are often correlated and unequally varied, generalized error structures were examined and compared to the independent and identically distributed (iid) structure. In addition, since the vast majority of forest models are developed to be used as predictive tools on new data once the model coefficients have been estimated, predictions from the fitted model with and without accounting for the generalized error structures were evaluated on both model fitting and independent model validation data sets. Results showed that, under the NLMMframework, the iid structure is a superior choice for addressing correlated and heteroscedastic errors, provided that the model is appropriate for the data. This outcome has important practical implications, as a simpler error structure can achieve better predictions than more complex structures. The theoretical and practical consequences of ignoring or accounting for the error structure in NLMM estimation and prediction are discussed.
机译：在这项研究中，我们演示了基于非线性混合模型（NLMM）技术的模型估计和预测程序。由于用于拟合森林模型的不均匀分布和不平衡的纵向数据通常是相关且不均匀变化的，因此对广义误差结构进行了检查，并将其与独立且均匀分布的（iid）结构进行了比较。此外，由于一旦模型系数被估算，绝大多数森林模型便被开发为新数据的预测工具，因此，在模型拟合和模型拟合中，对有或没有考虑广义误差结构的拟合模型的预测都进行了评估。独立的模型验证数据集。结果表明，在NLMM框架下，如果模型适合数据，则iid结构是解决相关和异方差错误的首选。这一结果具有重要的实际意义，因为较简单的错误结构比较复杂的结构可以实现更好的预测。讨论了在NLMM估计和预测中忽略或考虑错误结构的理论和实践后果。

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