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- W4360839274 abstract "The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for additive spatial autoregressive model with nonparametric endogenous effect. We construct the spatial autoregressive neural network (SARNN) to characterize spatial effects and fit the model. Our method is naturally applicable to linear or partially linear cases. Compared with other general spatial autoregression methods, our approach is competitive. Unlike normal black box neural networks, the architecture of SARNN allows itself to be interpretable with respect to each covariates. Compared with the spline method of nonparametric spatial autoregression, our method is more effective for different nonparametric functions and different distributions of spatial data. Extensive simulation studies and analysis of real data set highlights the usefulness of the proposed model and method." @default.
- W4360839274 created "2023-03-25" @default.
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- W4360839274 date "2023-06-01" @default.
- W4360839274 modified "2023-10-01" @default.
- W4360839274 title "Deep learning for the spatial additive autoregressive model with nonparametric endogenous effect" @default.
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- W4360839274 doi "https://doi.org/10.1016/j.spasta.2023.100743" @default.
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