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The Further Development of Stem Taper and Volume Models Defined by Stochastic Differential Equations

机译：随机微分方程定义的茎锥度和体积模型的进一步发展

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Stem taper process measured repeatedly among a series of individual trees is standardly analyzed by fixed and mixed regression models. This stem taper process can be adequately modeled by parametric stochastic differential equations (SDEs). We focus on the segmented stem taper model defined by the Gompertz, geometric Brownian motion and Ornstein-Uhlenbeck stochastic processes. This class of models enables the representation of randomness in the taper dynamics. The parameter estimators are evaluated by maximum likelihood procedure. The SDEs stem taper models were fitted to a data set of Scots pine trees collected across the entire Lithuanian territory. Comparison of the predicted stem taper and stem volume with those obtained using regression based models showed a predictive power to the SDEs models.
机译：通过固定和混合回归模型，标准地分析了在一系列单个树中重复测量的茎锥度过程。可以通过参数随机微分方程（SDE）充分建模此杆锥度过程。我们关注由Gompertz，几何布朗运动和Ornstein-Uhlenbeck随机过程定义的分段杆锥度模型。此类模型可以在锥度动力学中表示随机性。通过最大似然程序评估参数估计量。将SDE茎锥度模型拟合到整个立陶宛领土上收集的苏格兰松树数据集。将预测的茎锥度和茎体积与使用基于回归的模型获得的茎锥度和茎体积进行比较，表明对SDEs模型具有预测能力。

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