73 citations · 185 across the 8 of their papers we have counts for
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stat.CO2021
varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models
Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer
Gaussian processes (GPs) are well-known tools for modeling dependent data with applications in spatial statistics, time series analysis, or econometrics. In this article, we presen…
cs.LG2021
Latent Gaussian Model Boosting
Fabio Sigrist
Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potentia…
stat.ME2021
Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models
Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer
Spatially varying coefficient (SVC) models are a type of regression model for spatial data where covariate effects vary over space. If there are several covariates, a natural quest…