Scientific Program and Schedule

September 5, 2016

18.00-19.00     Registration
19.00-19.30 Opening
19.30-21.00 Welcome Cocktail

September 6, 2016

08.00-08.30     Registration

08.30-09.25     Keynote Lecture 1
Richard Samworth (UK)
Random projection ensemble classification
Chair: Angela Montanari

09.25-10.50     Talk Session 1
Robust estimation in model-based clustering
Chair: Christian Hennig

  • Andrea Cerioli, Luis Angel García-Escudero (Spain), Agustín Mayo-Iscar, Marco Riani
    Finding the Number of Groups in Model-Based Clustering via Constrained Likelihoods
  • Luis Angel García-Escudero, Francesca Greselin (Italy), Salvatore Ingrassia, Agustín Mayo-Iscar, Geoffrey McLachlan
    Recent results on robust estimation of mixtures
  • Andrea Cerioli, Luis Angel García-Escudero, Agustín Mayo-Iscar, Domenico Perrotta (JRC, EU), Francesca Torti
    Robust estimation and clustering of noisy regression mixtures

10.50-11.20     Coffee Break

11.20-12.45     Talk Session 2
Recent advances in mixture models for censored data
Chair: Víctor H. Lachos Dávila

  • Celso Rômulo Barbosa Cabral (Brazil), Víctor Hugo Lachos Davila, Luis Benites, Dipak Dey
    Robust Regression Modeling for Censored Data based on Mixtures of Student-t Distributions
  • Camila Borelli Zeller (Brazil), Celso Rômulo Barbosa Cabral, Víctor H. Lachos Davila
    Model-based clustering for high-dimensional regression data
  • Luis M. Castro (Chile), Wan-Lun Wang, Víctor Hugo Lachos Davila, Cristian L. Bayes, Vanda Inácio
    Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness

12.45-13.05     Lightning Talk Session 1
Chair: Roberto Di Mari

  • Silvia Bacci, Francesco Bartolucci, Silvia Pandolfi (Italy)
    Joint models for longitudinal and survival data: comparison between AR(1) and RIS formulation for the random effects
  • Michele Battisti, Antonio Punzo (Italy), Angelo Mazza, Salvatore Ingrassia
    Looking for the determinants of heterogeneity in education returns’ estimation: a semi-parametric approach
  • Yana Melnykov (USA)
    Studying the contribution of variables to the classification of individual observations
  • Paolo Berta (Italy), Veronica Vinciotti
    Cluster Weighted Models for Count Data
  • Massimo Cannas, Claudio Conversano (Italy), Francesco Mola, Emiliano Sironi
    Hospital differences in caesarean deliveries in Sardinia: case-mix or something more? A Bayesian non-parametric approach
  • Jan W. Owsiński (Poland), Karol Opara, Jarosław Stańczak, Sławomir Zadrożny
    Inverse clustering: exploring the space of clustering parameters to match the model data

13.05-14.05     Lunch

14.05-15.30     Talk Session 3
Data Clustering
Chair: Sugnet Lubbe

  • Jean Patrick Baudry (France), Gilles Celeux
    What does ICL tell us about homogeneity for Model-Based Clustering?
  • Pietro Coretto (Italy)
    Covariance matrix constraints in model-based clustering
  • Francesca Fortunato (Italy), Laura Anderlucci, Angela Montanari
    Learning a frontier: some developments in one-class classification

15.30-16.25     Keynote Lecture 2
Christophe Biernacki (France)
Unifying Data Units and Models in Statistics
Chair: Agustin Mayo-Iscar

16.25-16.55     Coffee Break

16.55-18.20     Talk Session 4
Issues in mixture models
Chair: Brendan Murphy

  • Paul McNicholas, Francesco Palumbo (Italy), Cristina Tortora
    A Probabilistic Distance Algorithm for Gaussian Mixture Parameter Estimation
  • Semhar K. Michael (USA)
    An effective strategy for initializing the EM algorithm in finite mixture models
  • Volodymyr Melnykov (USA)
    On Manly mixture modeling with extensions

18.20-18.45     Lightning Talk Session 2
Chair: Yana Melnikov

  • D. Aristei, Silvia Bacci (Italy), Francesco Bartolucci, Silvia Pandolfi
    Mixture growth modeling of households’ investment in risky financial assets
  • Roberto Di Mari (Italy), Roberto Rocci, Stefano Antonio Gattone
    Finite mixture of linear regression models: an adaptive constrained approach to maximum likelihood estimation
  • Christophe Biernacki, Alexandre Lourme (France)
    Satble and Non Stable Clustering Models
  • Leonardo Egidi (Italy), Roberta Pappadà, Francesco Pauli, Nicola Torelli
    A pivotal relabelling solution for label switching problem in Bayesian finite mixture models
  • Marta Nai Ruscone (Italy)
    Mixture of vine copulas for clustering
  • Francesca Torti (JRC, EU), Domenico Perrotta, Andrea Cerioli, Marco Riani
    Assessing the TCLUST methodology for robust clusterwise linear regression data

20.30     Workshop Dinner

September 7, 2016

8.45-9.40     Keynote Lecture 3
David Hunter (USA)
Clustering via Nonparametric Mixture Models
Chair: Maurizio Vichi

9.40-10.35     Keynote Lecture 4
Marco Alfò (Italy)
On finite mixtures of regression models for longitudinal data
Chair: Gilles Celeux

10.35-11.05     Coffee Break

11.05-12.30     Talk Session 5
Recent advances for finite mixtures
Chair: Sylvia Frühwirth-Schnatter

  • James Ng, Thomas Brendan Murphy (Ireland)
    Latent Space Stochastic Block Model for Social Networks
  • Sylvia Frühwirth-Schnatter (Austria)
    Some recent advances for Sparse Finite Mixtures
  • David Rossell (UK)
    Choosing mixture components via non-local priors

12.30-12.50     Lightning Talk Session 3
Chair: Aghiles Salah

  • Leo Cremonezi (UK)
    High definition customers. How to get more value from your market segmentation
  • Tae Rim Lee (Korea)
    Symbolic Data Analysis for Disease Prognosis with SNP data
  • Carlo Drago (Italy)
    Model Based- Time Series Anomaly Detection using Constrained Clustering
  • Oumaima Alaoui Ismaili (France), Vincent Lemaire, Antoine Cornuéjols
    Evaluation of predictive clustering quality
  • Edoardo Otranto, Massimo Mucciardi (Italy)
    The flexible space-time model
  • Carmela Cappelli (Italy), Rosaria Simone, Francesca Di Iorio
    CUB model trees for ordinal response: preliminary results

12.50-13.50     Lunch

13.50-15.15     Talk Session 6
Modeling high-dimensional and sparse data
Chair: Volodymyr Melnikov

  • Sharon X. Lee, K.L. Leemaqz, Geoff J. McLachlan (Australia)
    Fitting Mixture Models to Large Data Sets
  • Michael Fop (Ireland), Thomas Brendan Murphy
    Model-based Clustering with Sparse Covariance Matrices
  • Aghiles Salah (France), Mohamed Nadif
    Model-based von Mises-Fisher Co-clustering

15.15-16.10     Keynote Lecture 5
Christian Hennig (UK)
Gaussian and not-so-Gaussian clustering with robustness against outliers and a stab at the number of clusters
Chair: Geoff McLachlan

16.10-17.10     Poster Session with Coffee Break

17.10-18.35     Talk Session 7
Models for mixted-type, ordinal and network data
Chair: Tae Rim Lee

  • Monia Ranalli (Italy), Roberto Rocci
    A mixture model for mixed-type data: a case study
  • Daniel Fernández (New Zealand)
    Mixture-based Clustering for Ordinal Data
  • Francesco Bartolucci, Maria Francesca Marino (Italy), Silvia Pandolfi
    Dynamic Stochastic Blockmodels for network data: a composite likelihood approach

18.35-18.40     Closing

18.40-19.45     Wine Session
Chair: Mariella Ferrara (Destro, Masseria Setteporte, Nicosia, Tenuta Monte Gorna, Vivera)