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Model-based clustering based on sparse finite Gaussian mixtures
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 and …
distributions, we present a joint approach to estimate the number of mixture components and …
Dealing with label switching under model uncertainty
K∑ k= 1 ηkfT (y| θk),(10.1) where y is the realisation of a univariate or multivariate, discrete-
or continuousvalued random variable and the component densities fT (y| θk) arise from the …
or continuousvalued random variable and the component densities fT (y| θk) arise from the …
label. switching: An R package for dealing with the label switching problem in MCMC outputs
Label switching is a well-known and fundamental problem in Bayesian estimation of mixture
or hidden Markov models. In case that the prior distribution of the model parameters is the …
or hidden Markov models. In case that the prior distribution of the model parameters is the …
Label switching in Bayesian mixture models: Deterministic relabeling strategies
Label switching is a well-known problem in the Bayesian analysis of mixture models. On the
one hand, it complicates inference, and on the other hand, it has been perceived as a …
one hand, it complicates inference, and on the other hand, it has been perceived as a …
Modeling predictors of latent classes in regression mixture models
The purpose of this study is to provide guidance on a process for including latent class
predictors in regression mixture models. We first examine the performance of current …
predictors in regression mixture models. We first examine the performance of current …
Model-based clustering
This chapter introduces the model-based clustering is related to standard heuristic clustering
methods and an overview of different ways to specify the cluster model. It provides the …
methods and an overview of different ways to specify the cluster model. It provides the …
A topic-based segmentation model for identifying segment-level drivers of star ratings from unstructured text reviews
Online reviews provide rich information on customer satisfaction, displaying various numeric
ratings as well as detailed explanations presented in written form. However, analyzing such …
ratings as well as detailed explanations presented in written form. However, analyzing such …
Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance
Clustering longitudinal features is a common goal in medical studies to identify distinct
disease developmental trajectories. Compared to clustering a single longitudinal feature …
disease developmental trajectories. Compared to clustering a single longitudinal feature …
Clustering of longitudinal data: A tutorial on a variety of approaches
During the past two decades, methods for identifying groups with different trends in
longitudinal data have become of increasing interest across many areas of research. To …
longitudinal data have become of increasing interest across many areas of research. To …
Robust mixture regression modeling based on scale mixtures of skew-normal distributions
The traditional estimation of mixture regression models is based on the assumption of
normality (symmetry) of component errors and thus is sensitive to outliers, heavy-tailed …
normality (symmetry) of component errors and thus is sensitive to outliers, heavy-tailed …