论文标题
使用多个辅助变量的非平稳随机函数的嵌入式模型估计器
An Embedded Model Estimator for Non-Stationary Random Functions using Multiple Secondary Variables
论文作者
论文摘要
开发了使用多个次要变量的非平稳空间建模算法。它结合了地统计和分位数随机森林，提供了新的插值和随机模拟算法。本文介绍了该方法，并表明它具有与适用于地统计建模和分位数随机森林的一致性结果。该方法允许嵌入更简单的插值技术，例如Kriging，以进一步调节模型。该算法通过估计每个目标位置的目标变量的条件分布来起作用。这种分布的家族称为目标变量的信封。由此，可以获得空间估计，分位数和不确定性。还开发了一种从包膜中产生条件模拟的算法。因此，当它们从包膜中采样时，实现受到次要变量，趋势和可变性的相对重要性的相对变化的局部影响。
An algorithm for non-stationary spatial modelling using multiple secondary variables is developed. It combines Geostatistics with Quantile Random Forests to give a new interpolation and stochastic simulation algorithm. This paper introduces the method and shows that it has consistency results that are similar in nature to those applying to geostatistical modelling and to Quantile Random Forests. The method allows for embedding of simpler interpolation techniques, such as Kriging, to further condition the model. The algorithm works by estimating a conditional distribution for the target variable at each target location. The family of such distributions is called the envelope of the target variable. From this, it is possible to obtain spatial estimates, quantiles and uncertainty. An algorithm to produce conditional simulations from the envelope is also developed. As they sample from the envelope, realizations are therefore locally influenced by relative changes of importance of secondary variables, trends and variability.
