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- Gregor Kastner, Sylvia Frühwirth-Schnatter
- Computational Statistics & Data Analysis
- 2014

Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show… (More)

- 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)

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, Regina Tüchler
- Statistics and Computing
- 2008

- 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)

- Sylvia Frühwirth-Schnatter, Rudolf Frühwirth
- Computational Statistics & Data Analysis
- 2007

- Sylvia Frühwirth-Schnatter, Rudolf Frühwirth, Leonhard Held, Håvard Rue
- Statistics and Computing
- 2009

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)

Papers published in this report series are preliminary versions of journal articles and not for quotations. Abstract In the present paper 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 MCMC samplers that are obtained from full… (More)

- Sylvia Frühwirth-Schnatter, Helga Wagner
- Computational Statistics & Data Analysis
- 2008

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)