73 citations · 185 across the 8 of their papers we have counts for
13 papers
Simultaneous estimation of the effective reproduction number and the time series of daily infections: Application to Covid-19
Hans R. Künsch, Fabio Sigrist
The time-varying effective reproduction number is an important parameter for communication and policy decisions during an epidemic. In this paper, we present new statistical method…
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables
Fabio Sigrist
High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few d…
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…
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…
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…
Gaussian Process Boosting
Fabio Sigrist
We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean fu…
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…
Grabit: Gradient Tree-Boosted Tobit Models for Default Prediction
Fabio Sigrist, Christoph Hirnschall
A frequent problem in binary classification is class imbalance between a minority and a majority class such as defaults and non-defaults in default prediction. In this article, we…
Stochastic partial differential equation based modelling of large space-time data sets
Fabio Sigrist, Hans R. Künsch, Werner A. Stahel
Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advectio…
A dynamic nonstationary spatio-temporal model for short term prediction of precipitation
Fabio Sigrist, Hans R. Künsch, Werner A. Stahel
Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in m…
Using The Censored Gamma Distribution for Modeling Fractional Response Variables with an Application to Loss Given Default
Fabio Sigrist, Werner A. Stahel
Regression models for limited continuous dependent variables having a non-negligible probability of attaining exactly their limits are presented. The models differ in the number of…