Mixed effects machine learning with GPBoost for grouped and areal spatial econometric data

The GPBoost algorithm extends linear mixed effects and Gaussian process models by replacing the linear fixed effects function with a non-parametric non-linear function modeled using tree-boosting. This article shows how the GPBoost algorithm implemented in the GPBoost library can be used for modeling data with a spatial and grouped structure. We demonstrate the functionality of the GPBoost library using European GDP data which is an example of areal spatial econometric data.

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Tags: Databases