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[HWS19] Spatial autoregressive model for compositional data
Revue Nationale avec comité de lecture : Journal Journal of Beijing University of Aeronautics and Astronautics, vol. 45(1), pp. 93-98, 2019, (doi:10.13700/j.bh.1001-5965.2018.0253)Mots clés: compositional data, isometric logratio (ilr) transformation, maximum likelihood estimation, spatial dependence,spatial autoregressive model
Résumé:
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.
Commentaires:
in chinese
BibTeX
| @article { | |||
| HWS19, | |||
| title | = | "{Spatial autoregressive model for compositional data}", | |
| author | = | "T. Huang and H. Wang and G. Saporta", | |
| journal | = | "Journal of Beijing University of Aeronautics and Astronautics", | |
| year | = | 2019, | |
| volume | = | 45, | |
| number | = | 1, | |
| pages | = | "93-98", | |
| note | = | "{in chinese}", | |
| doi | = | "10.13700/j.bh.1001-5965.2018.0253", | |
| } | |||
