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Two-Component Mixture Models for Diameter Distributions in Mixed-Species, Two-Age Cohort Stands

机译:混合物种,两年龄组队列中直径分布的两成分混合模型

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The objectives of this study were to investigate the suitability of two-component Weibull and gamma mixtures to model the dbh distribution of a mixed-species, two-age cohort stands and for age cohort determination and to compare several methods to choose initial parameter values for maximum likelihood estimation of mixture models. Investigations were carried out in near-natural, fir (Abies alba Mill.)-beech (Fagus sylvatica L.), two-age cohort stands in the Swietokrzyski National Park (Central Poland), where unusually high mortality of fir followed by its recovery and revitalization has been observed. The age cohort YG1 is composed of trees from 60 to similar to 150 years of breast height age, and the age cohort YG2 is composed of trees less than 60 years of breast height age. The empirical distributions for the stands in this study were equally well fit by both the mixture Weibull and gamma models. It has been assumed that the estimated values, the weights (fractions), the means, and the standard deviations of two-component mixture models, are the predicted values of dbh statistics of age cohorts. The mean absolute relative errors used to evaluate this assumption were least for age cohort YG2 (from 14.8 to 29.6%) and largest for age cohort YG1 (from 17.7 to 45.0%). The dbh component I of mixture models can be identified in the stands investigated with age cohort YG2 and to a lesser degree the dbh component 2 with age cohort YG1. The multistart method for choosing initial values for the numerical procedure (a combination of the expectation-maximization algorithm with the Newton-type method) was best but also the most labor-intensive. The optimal way to estimate parameters in two-component mixtures with the Weibull or the gamma distributions is to apply min/max and 0.5/1.5/mean methods and, additionally, but only if necessary, a multistart method. FOR. SCI. 56(4):379-390.
机译:这项研究的目的是调查两组分威布尔和伽玛混合物对建模混合物种,两年龄队列站的dbh分布以及确定年龄队列的适用性,并比较几种选择初始参数值的方法的适用性。混合模型的最大似然估计。在Swietokrzyski国家公园(波兰中部)的两岁队列中,对近乎自然的冷杉(Abies alba Mill。)-山毛榉(Fagus sylvatica L.)山毛榉进行了调查,那里的冷杉死亡率异常高,随后恢复并观察到了振兴。年龄群组YG1由60至150岁的乳房高度年龄的树木组成,年龄群组YG2由小于60岁的乳房高度年龄的树木组成。 Weibull和gamma混合模型对本研究中的林分的经验分布同样很好。假设两组分混合模型的估计值,权重(分数),均值和标准偏差是年龄组的dbh统计量的预测值。用于评估此假设的平均绝对相对误差在YG2年龄组中最小(从14.8到29.6%),在YG1年龄组中最大(从17.7到45.0%)。混合模型的dbh组分I可以在年龄队列YG2调查的林分中确定,而在较小的程度上,年龄组YG1可以检测到dbh组分2。为数值过程选择初始值的多起点方法(期望最大化算法与牛顿型方法的组合)是最好的,但也是最费力的。估计具有威布尔或伽马分布的两组分混合物中参数的最佳方法是采用最小/最大和0.5 / 1.5 /平均值方法,此外,但仅在必要时使用多启动方法。对于。 SCI。 56(4):379-390。

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