Generalized Error Structure for Forestry Yield Models
Generalized Error Structure for Forestry Yield Models
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DOI：
10.1093/forestscience/33.2.423
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发表时间：
1987-06
期刊：
影响因子：
1.4
通讯作者：
T. Gregorie
中科院分区：
文献类型：
--
作者：
T. Gregorie
对永久森林地块重新测量数据的组合时间序列横截面性质进行了检查，目的是提高与这些数据拟合的产量模型的精度。线性模型误差项被视为图、时间段和残差随机效应的聚合，可能具有明显的方差和相关性。假设了四种替代误差协方差结构，它们的不同之处在于规定序列相关性、图方差异质性和交叉图相关性的方式。使用两阶段广义最小二乘法，以及在一种情况下使用完全最大似然估计，将具有假定误差协方差规范的产量模型拟合到一组 65 个纯、偶龄花旗松图重新测量中。使用普通最小二乘法结果作为比较的基础。通过预测误差和似然标准对拟合模型进行比较表明，普通最小二乘法几乎总是在前一种测量中表现更好，而一个或多个替代规范总是具有更高的似然性。为了。科学。 33（2）：423-444。
The combined time-series cross-sectional nature of remeasurement data from permanent forest plots is examined with an aim toward improving the precision of yield models fitted with these data. The linear model error term is regarded as an aggregation of plot, time period, and residual random effects with possibly distinct variances and correlations. Four alternative error covariance structures are posited that differ in the manner in which serial correlation, plot variance heterogeneity, and cross-plot correlations are prescribed. Yield models with the presumed error covariance specifications were fitted to a panel of 65 pure, even-aged Douglas-fir plot remeasurements, using two-stage generalized least squares and, in one case, a full maximum likelihood estimation. Ordinary least squares results were used as a basis for comparison. Comparison of the fitted models by prediction error and likelihood criteria indicate ordinary least squares nearly always performs better by the former measure, whereas one or more of the alternate specifications always have higher likelihood. For. Sci. 33(2):423-444.