Spatial autoregressive model for compositional data

成分数据的空间自回归模型

Tingting Huang (1) , Huiwen Wang (1) , Gilbert Saporta (2)

1 BUAA - Beihang University
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage

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.

Mots clés: spatial autoregressive model; spatial dependence; compositional data; isometric logratio (ilr) transformation; maximum likelihood estimation

Domaines: Statistiques [stat]

Tingting Huang, Huiwen Wang, Gilbert Saporta. 成分数据的空间自回归模型. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (1), pp.93-98. ⟨10.13700/j.bh.1001-5965.2018.0253⟩. ⟨hal-02471589⟩