Optimization of ordered distance sampling
Optimization of ordered distance sampling
复制标题
有序距离采样的优化
DOI：
10.1002/env.627
复制
发表时间：
2004
期刊：
影响因子：
1.7
通讯作者：
R. Engeman
中科院分区：
文献类型：
--
作者：
R. Nielson;R. Sugihara;T. Boardman;R. Engeman
有序距离采样是一种点到对象的采样方法，对于要求苛刻的现场情况来说，这种方法可以节省人力。进行了广泛的模拟研究，以求在有序距离抽样中从每个随机起点遇到的最优总体成员数g。蒙特卡洛模拟涵盖了四种空间模式、四种密度和四种样本大小的组合。对于每种情况，都考虑了从1到10的g值。计算每个g水平的相对均方根误差(RRMSE)和相对偏差，以RRMSE作为寻找g的最佳水平的主要评估标准。为密度估计推导出一个非参数的置信度区间，并将其包括在模拟中以评估其性能。
Ordered distance sampling is a point‐to‐object sampling method that can be labor‐efficient for demanding field situations. An extensive simulation study was conducted to find the optimum number, g, of population members to be encountered from each random starting point in ordered distance sampling. Monte Carlo simulations covered 64 combinations of four spatial patterns, four densities and four sample sizes. Values of g from 1 to 10 were considered for each case. Relative root mean squared error (RRMSE) and relative bias were calculated for each level of g, with RRMSE used as the primary assessment criterion for finding the optimum level of g. A non‐parametric confidence interval was derived for the density estimate, and this was included in the simulations to gauge its performance.