论文标题
随机森林的协方差回归
Covariance regression with random forests
论文作者
论文摘要
基于协变量的多变量响应载体之间的条件协方差或相关性对于包括神经科学，流行病学和生物医学在内的各个领域很重要。我们提出了一种新方法，称为随机森林（covregrf），以使用随机森林框架估算一个多变量响应的协方差矩阵。随机林木的建造是由专门设计的，旨在最大化儿童节点的样本协方差矩阵估计值之间的差异。我们还提出了对协变量子集的部分效应的显着性检验。我们通过一项模拟研究评估了提出的方法和显着性测试的性能，该研究表明，所提出的方法提供了准确的协方差矩阵估计，并且类型1误差得到了很好的控制。还提出了拟议方法在甲状腺疾病数据中的应用。 Covregrf在Cran的免费可用R软件包中实现。
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covariance matrix of a multivariate response given a set of covariates, using a random forest framework. Random forest trees are built with a splitting rule specially designed to maximize the difference between the sample covariance matrix estimates of the child nodes. We also propose a significance test for the partial effect of a subset of covariates. We evaluate the performance of the proposed method and significance test through a simulation study which shows that the proposed method provides accurate covariance matrix estimates and that the Type-1 error is well controlled. An application of the proposed method to thyroid disease data is also presented. CovRegRF is implemented in a freely available R package on CRAN.
