Areal prediction of survey data using Bayesian spatial generalised linear models

• Published in: Communications in statistics. Simulation and computation Vol. 49; no. 11; pp. 2963 - 2978
• Main Authors: Bakar, K. Shuvo, Jin, Huidong
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 01-11-2020; Taylor & Francis Ltd
• Subjects: areal prediction; Autoregressive models; Basis functions; Bayesian inference; Performance prediction; Spatial data; survey data
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2018.1530787

The conditional autoregressive approach is popular to analyse data with geocoded boundary. However, spatial prediction is often challenging when observed data are sparse. It becomes more challenging in predicting areal units with different areal boundaries. Hence, this paper develops a spatial generalised linear model for spatial predictions using data from spatially misaligned sparse locations. A spatial basis function associated with the conditional autoregressive models and the kriging method is considered. The proposed model demonstrates its better predictive performance through a simulation study and then is applied to understand the spatial pattern of undecided voting preferences in Australia.