成分数据的空间自回归模型
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  • 英文篇名:Spatial autoregressive model for compositional data
  • 作者:黄婷婷 ; 王惠文 ; SAPORTA ; Gilbert
  • 英文作者:HUANG Tingting;WANG Huiwen;SAPORTA Gilbert;School of Economics and Management,Beihang University;Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations;Beijing Advanced Innovation Center for Big Data and Brain Computing,Beihang University;Centre d'études et de Recherche en Informatique et Communications,Conservatoire National des Arts et Métiers;
  • 关键词:成分数据 ; 等距对数比(ilr)变换 ; 极大似然估计 ; 空间依赖 ; 空间自回归模型
  • 英文关键词:compositional data;;isometric logratio(ilr) transformation;;maximum likelihood estimation;;spatial dependence;;spatial autoregressive model
  • 中文刊名:BJHK
  • 英文刊名:Journal of Beijing University of Aeronautics and Astronautics
  • 机构:北京航空航天大学经济与管理学院;城市运行应急保障模拟技术北京市重点实验室;北京航空航天大学大数据科学与脑机智能高精尖创新中心;法国国立工艺学院计算机和通信研究中心;
  • 出版日期:2018-08-23 10:38
  • 出版单位:北京航空航天大学学报
  • 年:2019
  • 期:v.45;No.311
  • 基金:国家自然科学基金(71420107025)~~
  • 语种:中文;
  • 页:BJHK201901012
  • 页数:6
  • CN:01
  • ISSN:11-2625/V
  • 分类号:96-101
摘要
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.
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