5 papers
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…
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…
Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction
Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer
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…
KTBoost: Combined Kernel and Tree Boosting
Fabio Sigrist
We introduce a novel boosting algorithm called `KTBoost' which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree o…
Gradient and Newton Boosting for Classification and Regression
Fabio Sigrist
Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updat…