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

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Abstract

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 λ and other parameters in the models. The residuals of the models were analyzed through Moran’s I and Geary’s c.

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Authors and Affiliations

  1. School of Public Health, The University of Texas, Houston, TX, 77030, USA

    Dejian Lai

  2. Faculty of Statistics, Jiangxi University of Finance and Economics, Nanchang, China

    Dejian Lai

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  1. Dejian Lai
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Correspondence to Dejian Lai.

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Lai, D. Box–Cox transformation for spatial linear models: a study on lattice data. Stat Papers 51, 853–864 (2010). https://doi.org/10.1007/s00362-008-0178-4

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  • DOI: https://doi.org/10.1007/s00362-008-0178-4

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