299 citations · 718 across the 15 of their papers we have counts for
18 papers
Marginal Data Augmentation for Efficient Bayesian Modeling of Counts and Rates with a Demographic Application
Gregor Zens, Sylvia Frühwirth-Schnatter
Count data models are ubiquitous in many fields, yet Bayesian data augmentation algorithms for such models frequently encounter challenges with Markov chain Monte Carlo efficiency.…
CliPS -- How to identify cluster distributions in Bayesian mixture models
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
We propose the CliPS procedure when fitting Bayesian mixture models in the context of model-based clustering to identify the cluster distributions while simultaneously assessing th…
Without Pain -- Clustering Categorical Data Using a Bayesian Mixture of Finite Mixtures of Latent Class Analysis Models
Gertraud Malsiner-Walli, Bettina Grün, Sylvia Frühwirth-Schnatter
We propose a Bayesian approach for model-based clustering of multivariate categorical data where variables are allowed to be associated within clusters and the number of clusters i…
Dynamic Mixture of Finite Mixtures of Factor Analysers with Automatic Inference on the Number of Clusters and Factors
Margarita Grushanina, Sylvia Frühwirth-Schnatter
Mixtures of factor analysers (MFA) models represent a popular tool for finding structure in data, particularly high-dimensional data. While in most applications the number of clust…
Generalized Cumulative Shrinkage Process Priors with Applications to Sparse Bayesian Factor Analysis
Sylvia Frühwirth-Schnatter
The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (20…
Sparse Bayesian factor analysis when the number of factors is unknown
Sylvia Frühwirth-Schnatter, Darjus Hosszejni, Hedibert Freitas Lopes
There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonit…
How many data clusters are in the Galaxy data set? Bayesian cluster analysis in action
Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter
In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (2001) compares maximum likel…
Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis
Jan Greve, Bettina Grün, Gertraud Malsiner-Walli +1
Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture mo…
Ultimate Pólya Gamma Samplers -- Efficient MCMC for possibly imbalanced binary and categorical data
Gregor Zens, Sylvia Frühwirth-Schnatter, Helga Wagner
Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been av…
Generalized mixtures of finite mixtures and telescoping sampling
Sylvia Frühwirth-Schnatter, Gertraud Malsiner-Walli, Bettina Grün
Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures (MFMs), i.e., finite mixtures with a prior on the number of components, is performed. This…
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…
Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
Gregor Kastner, Sylvia Frühwirth-Schnatter
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 uti…
From here to infinity - sparse finite versus Dirichlet process mixtures in model-based clustering
Sylvia Frühwirth-Schnatter, Gertraud Malsiner-Walli
In model-based-clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al (2016) are sparse fi…
Achieving Shrinkage in a Time-Varying Parameter Model Framework
Angela Bitto, Sylvia Frühwirth-Schnatter
Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying parameters to static ones, if the m…
Model-based clustering based on sparse finite Gaussian mixtures
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components an…
Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert Freitas Lopes
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two inte…
Identifying Mixtures of Mixtures Using Bayesian Estimation
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal compon…