Sylvia Frühwirth‐Schnatter
Biographic Data
| ID | 9107539 |
|---|---|
| NAME | Sylvia Frühwirth‐Schnatter |
| GIVEN NAMES | Sylvia |
| FAMILY NAME | Frühwirth‐Schnatter |
| SIGNATURE | SCHNATTER S F |
| AFFILIATIONS | Johannes Kepler University of Linz |
| ORCID | 0000-0003-0516-5552 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2008 |
| H-INDEX | 0 |
Model-Based Clustering of Multiple Time Series
We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest to estimate the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on m…
Bayesian Analysis of the Heterogeneity Model
We consider Bayesian estimation of a finite mixture of models with random effects, which is also known as the heterogeneity model. First, we discuss the properties of various Markov chain Monte Carlo samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler …
No prominent works on this page.
Bayesian Analysis of the Heterogeneity Model
We consider Bayesian estimation of a finite mixture of models with random effects, which is also known as the heterogeneity model. First, we discuss the properties of various Markov chain Monte Carlo samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler …
Model-Based Clustering of Multiple Time Series
We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest to estimate the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on m…
Bayesian probability (2 works) · Computer Science (2 works) · Econometrics (2 works) · Markov chain (2 works) · Markov chain Monte Carlo (2 works) · Mathematics (2 works) · Applied Mathematics (1 work) · Artificial Intelligence (1 work) · Bayesian Methods and Mixture Models (1 work) · Cluster analysis (1 work)