Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction

@article{Dambon2020MaximumLE,
  title={Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction},
  author={Jakob A. Dambon and Fabio Sigrist and Reinhard Furrer University of Zurich and Lucerne University of Applied Sciences and Arts},
  journal={arXiv: Methodology},
  year={2020}
}
In regression models for spatial data, it is often assumed that the marginal effects of covariates on the response are constant over space. In practice, this assumption might often be questionable. In this article, we show how a Gaussian process-based spatially varying coefficient (SVC) model can be estimated using maximum likelihood estimation (MLE). In addition, we present an approach that scales to large data by applying covariance tapering. We compare our methodology to existing methods… Expand

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