Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria.
Oluyemi A Okunlola, Mohannad Alobid, Olusanya E Olubusoye, Kayode Ayinde, Adewale F Lukman, István Szűcs
Author Information
Oluyemi A Okunlola: Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State, Nigeria.
Mohannad Alobid: Faculty of Economics and Business, Institute of Applied Economic Sciences, University of Debrecen, 4032, Debrecen, Hungary. mohannad.alobid@econ.unideb.hu.
Olusanya E Olubusoye: Department of Statistics, University of Ibadan, Ibadan, Oyo State, Nigeria.
Kayode Ayinde: Department of Statistics, Federal University of Technology, Akure, Ondo State, Nigeria.
Adewale F Lukman: Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State, Nigeria.
István Szűcs: Faculty of Economics and Business, Institute of Applied Economic Sciences, University of Debrecen, 4032, Debrecen, Hungary.
In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.
