7 citations · 8 across the 6 of their papers we have counts for
6 papers
Going With the Flow: Normalizing Flows for Gaussian Process Regression under Hierarchical Shrinkage Priors
Peter Knaus
Gaussian Process Regression (GPR) is a powerful tool for nonparametric regression, but its application in a fully Bayesian fashion in high-dimensional settings is hindered by two p…
The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models
Peter Knaus, Sylvia Frühwirth-Schnatter
Many existing shrinkage approaches for time-varying parameter (TVP) models assume constant innovation variances across time points, inducing sparsity by shrinking these variances t…
Sparse Bayesian State-Space and Time-Varying Parameter Models
Sylvia Frühwirth-Schnatter, Peter Knaus
In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both cont…
Flexible yet Sparse Bayesian Survival Models with Time-Varying Coefficients and Unobserved Heterogeneity
Peter Knaus, Daniel Winkler, Sebastian F. Schoppmann +1
Survival analysis is an important area of medical research, yet existing models often struggle to balance simplicity with flexibility. Simple models require minimal adjustments but…
Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models
Annalisa Cadonna, Sylvia Frühwirth-Schnatter, Peter Knaus
Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of a predictor on the outcome variable. However, in particular when the number of p…
Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna +1
Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TV…