research
∙
06/12/2023
Compatibility of Space-Time Kernels with Full, Dynamical, or Compact Support
We deal with the comparison of space-time covariance kernels having, eit...
share
research
∙
03/07/2022
Dominant-feature identification in data from Gaussian processes applied to Finnish forest inventory records
In spatial data, location-dependent variation leads to connected structu...
research
∙
12/23/2021
Consistency and asymptotic normality of covariance parameter estimators based on covariance approximations
For a zero-mean Gaussian random field with a parametric covariance funct...
research
∙
06/19/2021
Discussion on Competition for Spatial Statistics for Large Datasets
We discuss the experiences and results of the AppStatUZH team's particip...
research
∙
06/04/2021
varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models
Gaussian processes (GPs) are well-known tools for modeling dependent dat...
research
∙
01/06/2021
Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models
Spatially varying coefficient (SVC) models are a type of regression mode...
research
∙
10/01/2020
Bayesian spatial modelling of terrestrial radiation in Switzerland
The geographic variation of terrestrial radiation can be exploited in ep...
research
∙
07/29/2020
Asymptotically Equivalent Prediction in Multivariate Geostatistics
Cokriging is the common method of spatial interpolation (best linear unb...
research
∙
06/12/2020
Identification of Dominant Features in Spatial Data
Dominant features of spatial data are structures or patterns that become...
research
∙
05/29/2020
Multiresolution Decomposition of Areal Count Data
Multiresolution decomposition is commonly understood as a procedure to c...
research
∙
01/22/2020
Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction
In regression models for spatial data, it is often assumed that the marg...
research
∙
11/25/2019
Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes
The asymptotic analysis of covariance parameter estimation of Gaussian p...
research
∙
11/20/2019
Additive Bayesian Network Modelling with the R Package abn
The R package abn is designed to fit additive Bayesian models to observa...
research
∙
06/02/2019
Combining Heterogeneous Spatial Datasets with Process-based Spatial Fusion Models: A Unifying Framework
In modern spatial statistics, the structure of data that is collected ha...
research
∙
02/18/2019
Is a single unique Bayesian network enough to accurately represent your data?
Bayesian network (BN) modelling is extensively used in systems epidemiol...
research
∙
09/18/2018
Comparison between Suitable Priors for Additive Bayesian Networks
Additive Bayesian networks are types of graphical models that extend the...
research
∙
08/03/2018
Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology
Bayesian network modelling is a well adapted approach to study messy and...
research
∙
04/30/2018
eggCounts: a Bayesian hierarchical toolkit to model faecal egg count reductions
This is a vignette for the R package eggCounts version 2.0. The package ...
research
∙
04/30/2018
optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim()
The R package optimParallel provides a parallel version of the gradient-...
research
∙
04/19/2018
