Box-Cox transformation for spatial linear models: a study on lattice data

In this article, we extended the classic Box-Cox transformation to spatial linear models. For a comparative study, the proposed models were applied to a real data set of Chinese population growth and economic development with three different structures: no spatial correction, conditional autoregressive and simultaneous autoregressive. Maximal likelihood method was used to estimate the Box-Cox parameter lambda and other parameters in the models. The residuals of the models were analyzed through Moran's I and Geary's c.