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A Spatial Durbin Model for Compositional Data

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. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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https://hal-cnam.archives-ouvertes.fr/hal-03274385
Contributor : Gilbert Saporta Connect in order to contact the contributor
Submitted on : Tuesday, July 6, 2021 - 6:55:08 PM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tingting Huang, Gilbert Saporta, Huiwen Wang. A Spatial Durbin Model for Compositional Data. Daouia A., Ruiz-Gazen A. Advances in Contemporary Statistics and Econometrics, Springer Nature, pp.471-488, 2021, 978-3-030-73249-3. ⟨10.1007/978-3-030-73249-3_24⟩. ⟨hal-03274385⟩

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