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A Spatial Durbin Model for Compositional Data

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Abstract

A compositional linear model (regression of a scalar response on a set of compositions) for areal data is proposed, where observations are not independent and present spatial autocorrelation. Specifically, we borrow thoughts from the spatial Durbin model considering that it produces unbiased coefficient estimates compared to other spatial linear regression models (including the spatial error model, the spatial autoregressive model, the Kelejian-Prucha model, and the spatial Durbin error model). The orthonormal log-ratio (olr) transformation based on a sequential binary partition of compositions and maximum likelihood estimation method are employed to estimate the new model. We also check the proposed estimators on a simulated and a real dataset.

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Acknowledgements

This research was financially supported by the National Natural Science Foundation of China under Grant No. 71420107025 and Capital University of Economics and Business under Grant No. XRZ2021040.

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Correspondence to Gilbert Saporta .

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Huang, T., Saporta, G., Wang, H. (2021). A Spatial Durbin Model for Compositional Data. In: Daouia, A., Ruiz-Gazen, A. (eds) Advances in Contemporary Statistics and Econometrics. Springer, Cham. https://doi.org/10.1007/978-3-030-73249-3_24

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