A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types. Issue 11 (2nd November 2021)

• Record Type: Journal Article
• Authors: Wang, Huiwen; Huang, Tingting; Wang, Shanshan
• Is Part Of: Communications in statistics. Volume 50:Issue 11(2021)
• Page Start: 3498
• Page End: 3515
• Publication Date: 2021-11-02
• Subjects: Compositional data -- FPCA -- Functional data -- ilr transformation -- Maximum likelihood estimation -- Spatial autoregressive model; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1626885
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: Mixed spatial autoregressive (SAR) models with numerical covariates have been well studied. However, as non-numerical data, such as functional data and compositional data, receive substantial amounts of attention and are applied to economics, medicine and meteorology, it becomes necessary to develop flexible SAR models with multiple data types. In this article, we integrate three types of covariates, functional, compositional and numerical, in an SAR model. The new model has the merits of classical functional linear models and compositional linear models with scalar responses. Moreover, we develop an estimation method for the proposed model, which is based on functional principal component analysis (FPCA), the isometric logratio (ilr) transformation and the maximum likelihood estimation (MLE) method. Monte Carlo experiments demonstrate the effectiveness of the estimators. A real dataset is also used to illustrate the utility of the proposed model.