229 citations · 535 across the 9 of their papers we have counts for
10 papers
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…
Bayesian Finite Mixture Models
Bettina Grün, Gertraud Malsiner-Walli
Finite mixture models are a useful statistical model class for clustering and density approximation. In the Bayesian framework finite mixture models require the specification of su…
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…
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
David Rügamer, Florian Pfisterer, Bernd Bischl +1
In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimi…
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
Paul Hofmarcher, Jan Vávra, Sourav Adhikari +1
Gentzkow, Shapiro and Taddy, Econometrica Vol 87, No 4, 2019 (henceforth GST) use a supervised text-based regression model to assess changes in partisanship in U.S. congressional s…
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…
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…
Model-based Clustering
Bettina Grün
Mixture models extend the toolbox of clustering methods available to the data analyst. They allow for an explicit definition of the cluster shapes and structure within a probabilis…
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…
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…