A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types

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.

Keywords:

• Compositional data
• FPCA
• Functional data
• ilr transformation
• Maximum likelihood estimation
• Spatial autoregressive model

Additional information

Funding

This research was financially supported by the National Natural Science Foundation of China under grant nos. 71420107025 and 11701023.