成分数据的空间自回归模型

Spatial autoregressive model for compositional data

针对已有成分数据线性回归模型对研究对象相互独立的严格要求,提出了含有成分数据和普通数据的空间自回归模型,在此基础上提出了成分数据空间自回归模型的估计方法。新模型结合了空间自回归模型处理因变量之间相互依赖的优势,可同时处理成分数据和普通数据。通过利用等距对数比(ilr)变换将成分数据解约束,得到了新模型的参数估计量。蒙特卡罗模拟实验验证了所提估计方法的有效性。

The existing compositional linear models assume that samples are independent,which is often violated in practice. To solve this problem,we put forward a spatial autoregressive model for compositional data,which contains both compositional covariates and scalar predictors. Furthermore,a new estimation method is proposed. The new model has advantages of coping with mixed compositional and numerical data and expressing dependence between the responses. And the parameter estimators are obtained through isometric logratio( ilr) transformation,which transforms dependent compositional data into independent real vector. A Monte-Carlo simulation experiment verifies the effectiveness of the proposed estimation method.