 Multiple linear regression with compositional response and covariatesJiajia Chen, Xiaoqin Zhang and Shengjia Li Journal of Applied Statistics, 2017, vol. 44, issue 12, 2270-2285 Abstract: The standard regression model designed for real space is not suitable for compositional variables; it should be considered, whether the response and/or covariates are of compositional nature. There are usually three types of multiple regression model with compositional variables: Type 1 refers to the case where all the covariates are compositional data and the response is real; Type 2 is the opposite of Type 1; Type 3 relates to the model with compositional response and covariates. There have been some models for the three types. In this paper, we focus on Type 3 and propose multiple linear regression models including model in the simplex and model in isometric log-ratio (ilr) coordinates. The model in the simplex is based on matrix product, which can project a $ D_{1} $ D1-part composition to another $ D_{2} $ D2-part composition, and can deal with different number of parts of compositional variables. Some theorems are given to point out the relationship of parameters between the proposed models. Moreover, the inference for parameters in proposed models is also given. Real example is studied to verify the validity and usefulness of proposed models. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:44:y:2017:i:12:p:2270-2285 Ordering information: This journal article can be ordered from DOI: 10.1080/02664763.2016.1157145 Access Statistics for this article Journal of Applied Statistics is currently edited by Robert Aykroyd More articles in Journal of Applied Statistics from Taylor & Francis Journals |
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