Scientific Program and Schedule
September 3, 2014
| 18.00-19.00 | Registration |
| 19.00-19.30 | Opening |
| 19.30-21.00 | Welcome Cocktail |
September 4, 2014
08.00-08.30 Registration
08.30-09.25 Keynote Lecture 1
Sylvia Frühwirth-Schnatter
Flexible modelling based on Sparse Finite Mixtures
Chair: Angela Montanari
09.25-10.50 Talk Session 1
Model-based clustering for ordinal, categorical and mixed type data
Chair: Christian Hennig
Improving latent class analysis through outlier detection– an example from criminal careers research
Variable selection to clustering ordinal data through a pairwise likelihood approach
Model-based clustering for the analysis of categorical data
10.50-11.20 Coffee Break
11.20-12.45 Talk Session 2
Probabilistic models for clustering
Chair: Hans-Hermann Bock
Two-way clustering by maximizing a cluster-specific interactions criterion
Model-based clustering for high-dimensional regression data
Clustering heteroscedastic data
12.45-13.05 Lightning Talk Session 1
Chair: Maria Francesca Marino
Robust mixtures of factor analyzers
Investigating the influence of difference priors choices for mixtures of Gaussian mixtures
Selection of GLM mixtures with a clustering approach
Comparing two non-Gaussian clustering models for high-dimensional data
Improved Initialisation of Model-Based Clustering Using a Gaussian Hierarchical Partition
Mixtures of coalesced generalized hyperbolic distributions
13.05-14.05 Lunch
14.05-15.30 Talk Session 3
Advances in mixture models
Chair: Roberto Rocci
Mixture of negative binomial distributions for modeling the overdispersion in RNA-SEQ data
On Finite Mixtures of Canonical Fundamental Skew t-Distributions
An integrated framework for simulating mixtures of multivariate and regression data
15.30-16.25 Keynote Lecture 2
Charles Bouveyron
Discriminative variable selection in model-based clustering
Chair: Gunter Ritter
16.25-16.50 Coffee Break
16.50-18.15 Talk Session 4
Clustering models for social sciences
Chair: Brian Francis
Micro-macro multilevel latent
class models to explain group-level outcomes using (discrete) individual-level variables
Unsupervised clustering of higher level units in multilevel linear models
Heterogeneity and healthcare structures effectivenes: The proposal of a cluster-weighted multilevel model
18.15-18.35 Lightning Talk Session 2
Chair: Zsuzsa Bakk
Modelling informative missingness in longitudinal data via latent drop-out quantile regressions
A comparison between the latent Markov and growth mixture models for the analysis of longitudinal data
Forecasting financial products acquisition via dynamic segmentation: a comparison between standard and
mixture latent class Markov models
A clustering method for functional data
A longitudinal study of Polish emigration attitudes using latent Markov model
Analyzing Cluster Algorithms on Functional Data with Sparse Measurements
20.30 Workshop Dinner
September 5, 2014
8.45-9.40 Keynote Lecture 3
Mohamed Nadif
Document clustering under different approaches
Chair: Maurizio Vichi
9.40-10.35 Keynote Lecture 4
Francesco Bartolucci
Model selection in generalized finite mixture regression models by Hausman testing
Chair: Geoff McLachlan
10.35-11.05 Coffee Break
11.05-12.30 Talk Session 5
Recent developments in clustering models
Chair: Francesca Greselin
Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters
Quantifying partition uncertainty for Dirichlet process Bayesian clustering
New challenges for modelling big data
12.30-12.50 Lightning Talk Session 3
Chair: Paula Murray
The robustness of bias-adjusted three-step latent class modeling with distal outcome variables
Sample size calculations for model-based supervised classification in ophthalmology
FlexCWM: a software package for Cluster-Weighted Modeling
Robust clustering tools for Machine Translation Quality Estimation
Multiple imputation of missing categorical data through latent class models
12.50-13.50 Lunch
13.50-14.50 Talk Session 6
Supervised and unsupervised learning
Chair: Gabriela Czanner
On the reliability of classification rules: a proposal based on the Beta regression model
On model-based clustering with membership constraints
14.50-15.45 Keynote Lecture 5
Agustín Mayo-Iscar
Trimming and restrictions for model based clustering: classical approaches and new direction
Chair: Andrea Cerioli
15.45-16.45 Poster Session with Coffee Break
16.45-18.30 Talk Session 7
New developments in non-Gaussian mixture modeling
Chair: Tsung-I Lin
Kimura 2-parameter ancestral mixture models for clustering DNA sequences
Some new tools for mixtures of common t-factor analyzers with its application
Robust modeling differential effects and non-Normal distribution through mixture of
skew-t regressions
Robust mixture regression modelling based on scale mixtures of skew-normal
distributions
18.30-18.40 Closing
18.40-20.00 Wine Session
Chair: Maurizio Miccichè (Calatrasi Winery)