Peter Knaus

Peter Knaus
Wirtschaftsuniversität Wien | WU · Department of Finance, Accounting and Statistics

Master of Science

About

6
Publications
674
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32
Citations
Citations since 2017
6 Research Items
32 Citations
201720182019202020212022202302468101214
201720182019202020212022202302468101214
201720182019202020212022202302468101214
201720182019202020212022202302468101214
Introduction

Publications

Publications (6)
Preprint
Full-text available
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 continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using...
Preprint
Dynamic survival models are a flexible tool for overcoming limitations of popular methods in the field of survival analysis. While this flexibility allows them to uncover more intricate relationships between covariates and the time-to-event, it also has them running the risk of overfitting. This paper proposes a solution to this issue based on stat...
Article
Full-text available
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 TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this pape...
Article
Full-text available
Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of explanatory variables on the outcome variable. However, in particular when the number of explanatory variables is large, there is a known risk of overfitting and poor predictive performance, since the effect of some explanatory variables is constant...
Preprint
Full-text available
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 predictors is large, there is a known risk of overfitting and poor predictive performance, since the effect of some predictors is constant over time. We propose a prior fo...
Preprint
Full-text available
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 TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this pape...

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