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- Sylvia Frühwirth-Schnatter, Helga Wagner
- 2011

- Sylvia Frühwirth-Schnatter, Sylvia Kaufmann
- 2004

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… (More)

- Sylvia Frühwirth-Schnatter, Hedibert Freitas, Lopes
- 2010

We introduce a new and general set of identifiability conditions for factor models which handles the ordering problem associated with current common practice. In addition, the new class of parsimonious Bayesian factor analysis leads to a factor loading matrix representation which is an intuitive and easy to implement factor selection scheme. We argue that… (More)

- Sylvia Frühwirth-Schnatter, Christoph Pamminger, Rudolf Winter-Ebmer, Andrea Weber
- 2010

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering,… (More)

The article proposes an improved method of auxiliary mixture sampling for count data, binomial data and multinomial data. In constrast to previously proposed samplers the method uses a limited number of latent variables per observation, independent of the intensity of the underlying Poisson process in the case of count data, or of the number of experiments… (More)

Several new estimators of the marginal likelihood for complex non-Gaussian models are developed. These estimators make use of the output of auxiliary mixture sampling for count data and for binary and multinomial data. One of these estimators is based on combining Chib's estimator with data augmentation as in auxiliary mixture sampling, while the other… (More)

- Markus Hahn, Sylvia Frühwirth-Schnatter, Jörn Sass
- 2007

We present Markov chain Monte Carlo methods for estimating parameters of multidimensional, continuous time Markov switching models. The observation process can be seen as a diffusion, where drift and volatility coefficients are modeled as continuous time, finite state Markov chains with a common state process. The states for drift and volatility and the… (More)