On selection of spatial linear models for lattice data

Jun Zhu, Hsin‐Cheng Huang and Perla E. Reyes

Journal of the Royal Statistical Society Series B, 2010, vol. 72, issue 3, 389-402

Abstract: Summary. Spatial linear models are popular for the analysis of data on a spatial lattice, but statistical techniques for selection of covariates and a neighbourhood structure are limited. Here we develop new methodology for simultaneous model selection and parameter estimation via penalized maximum likelihood under a spatial adaptive lasso. A computationally efficient algorithm is devised for obtaining approximate penalized maximum likelihood estimates. Asymptotic properties of penalized maximum likelihood estimates and their approximations are established. A simulation study shows that the method proposed has sound finite sample properties and, for illustration, we analyse an ecological data set in western Canada.

Date: 2010
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https://doi.org/10.1111/j.1467-9868.2010.00739.x

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