%PDF-1.5 % 1 0 obj << /S /GoTo /D (chapter*.1) >> endobj 4 0 obj (Committees) endobj 5 0 obj << /S /GoTo /D (section*.2) >> endobj 8 0 obj (Scientific) endobj 9 0 obj << /S /GoTo /D (section*.3) >> endobj 12 0 obj (Organizing) endobj 13 0 obj << /S /GoTo /D (chapter*.5) >> endobj 16 0 obj (List of Abstracts) endobj 17 0 obj << /S /GoTo /D (section*.6) >> endobj 20 0 obj (Talk Session 1: Benford's Law for Anomaly and Fraud Detection) endobj 21 0 obj << /S /GoTo /D (section*.7) >> endobj 24 0 obj (Statistical models and the Benford hypothesis: A unifying framework \(Lucio Barabesi, Andrea Cerioli, and Marco Di Marzio\)) endobj 25 0 obj << /S /GoTo /D (section*.9) >> endobj 28 0 obj (Benford goes multivariate: A new fraud detection method, with application to music streaming data \(Peter Filzmoser and Nermina Mumic\)) endobj 29 0 obj << /S /GoTo /D (section*.11) >> endobj 32 0 obj (Who is afraid of the probability-savvy fraudster? \(Andrea Cerioli, Lucio Barabesi, Andrea Cerasa, and Domenico Perrotta\)) endobj 33 0 obj << /S /GoTo /D (section*.13) >> endobj 36 0 obj (Keynote Lecture 1) endobj 37 0 obj << /S /GoTo /D (section*.14) >> endobj 40 0 obj (Robustness issues in subspace clustering \(Luis Angel Garc\355a-Escudero\)) endobj 41 0 obj << /S /GoTo /D (section*.16) >> endobj 44 0 obj (Talk Session 2: Robust Model-based Clustering and Outlier Detection) endobj 45 0 obj << /S /GoTo /D (section*.17) >> endobj 48 0 obj (Monitoring the robust Cluster-Weighted model \(Andrea Cappozzo Luis Angel Garc\355a Escudero Francesca Greselin and Agust\355n Mayo-Iscar\)) endobj 49 0 obj << /S /GoTo /D (section*.19) >> endobj 52 0 obj (Banks' business models in the euro area: a cluster analysis in high dimensions \(Matteo Farn\350 and Angelos Vouldis\)) endobj 53 0 obj << /S /GoTo /D (section*.21) >> endobj 56 0 obj (Directional Outlier Detection and Robust Model-Based Clustering with Missing Data \(Hung Tong and Cristina Tortora\)) endobj 57 0 obj << /S /GoTo /D (section*.23) >> endobj 60 0 obj (Keynote Lecture 2) endobj 61 0 obj << /S /GoTo /D (section*.24) >> endobj 64 0 obj (Perturb and Conquer: how Classification Can Benefit from Data Perturbation \(Angela Montanari and Laura Anderlucci\)) endobj 65 0 obj << /S /GoTo /D (section*.26) >> endobj 68 0 obj (Talk Session 3: Elliptical Mixture Models) endobj 69 0 obj << /S /GoTo /D (section*.27) >> endobj 72 0 obj (A class of parsimonious Gaussian mixture models with an extended ultrametric covariance structure \(Carlo Cavicchia*, Maurizio Vichi\201 and Giorgia Zaccaria\202\)) endobj 73 0 obj << /S /GoTo /D (section*.29) >> endobj 76 0 obj (Nonparametric consistency for ML estimation and clustering with finite mixtures of elliptically symmetric distributions \(Pietro Coretto and Christian Hennig\)) endobj 77 0 obj << /S /GoTo /D (section*.31) >> endobj 80 0 obj (Gaussian Mixtures Using Range-Logit Transformation with an Application to Image Segmentation \(Luca Scrucca\)) endobj 81 0 obj << /S /GoTo /D (section*.33) >> endobj 84 0 obj (Talk Session 4: Latent Class Models) endobj 85 0 obj << /S /GoTo /D (section*.34) >> endobj 88 0 obj (Multilevel Latent Class Models for cross-classified data \(Silvia Columbu and Jeroen K. Vermunt\)) endobj 89 0 obj << /S /GoTo /D (section*.36) >> endobj 92 0 obj (Capturing Correlated Clusters Using Mixtures of Latent Class Models \(Sylvia Fr\374hwirth-Schnatter, Bettina Gr\374n, and Gertraud Malsiner-Walli\)) endobj 93 0 obj << /S /GoTo /D (section*.38) >> endobj 96 0 obj (A three-step rectangular Latent Markov modelling for a recommender system in self-learning platforms \(Rosa Fabbricatore, Roberto Di Mari, Zsuzsa Bakk, Mark de Rooij, and Francesco Palumbo\)) endobj 97 0 obj << /S /GoTo /D (section*.40) >> endobj 100 0 obj (Keynote Lecture 3) endobj 101 0 obj << /S /GoTo /D (section*.41) >> endobj 104 0 obj (Robust selection of the number of clusters and of the clustering structure using Bayesian methods \(Judith Rousseau, Dan Moss, and Arthur Hita\)) endobj 105 0 obj << /S /GoTo /D (section*.43) >> endobj 108 0 obj (Lightning Talk Session 1) endobj 109 0 obj << /S /GoTo /D (section*.44) >> endobj 112 0 obj (Bayesian semiparametric finite mixture of regression models \(Marco Berrettini, Giuliano Galimberti, and Saverio Ranciati\)) endobj 113 0 obj << /S /GoTo /D (section*.46) >> endobj 116 0 obj (A stochastic blockmodel for hypergraphs \(Luca Brusa and Catherine Matias\)) endobj 117 0 obj << /S /GoTo /D (section*.48) >> endobj 120 0 obj (On finite mixture modeling of change-point processes \(Yana Melnykov and Xuwen Zhu\)) endobj 121 0 obj << /S /GoTo /D (section*.50) >> endobj 124 0 obj (Model-based clustering for network-linked data \(Iuliia Promskaia, Adrian O'Hagan, and Michael Fop\)) endobj 125 0 obj << /S /GoTo /D (section*.52) >> endobj 128 0 obj (Open Banking Challenges for Clustering and Classification \(Galina Andreeva\)) endobj 129 0 obj << /S /GoTo /D (section*.54) >> endobj 132 0 obj (Robustness issues for semi-supervised learning of financial frauds \(Francesca Torti, Gabrielle Voiseaux, and Domenico Perrotta\)) endobj 133 0 obj << /S /GoTo /D (section*.56) >> endobj 136 0 obj (Learning to Classify From Almost No Data \(Ilia Sucholutsky, Thomas L. Griffiths\)) endobj 137 0 obj << /S /GoTo /D (section*.58) >> endobj 140 0 obj (Talk Session 5: Supervised and Unsupervised Learning for Anomaly and Fraud Detection) endobj 141 0 obj << /S /GoTo /D (section*.59) >> endobj 144 0 obj (Variational Inference of a Stochastic Bloc Model for a Sequence of Graphs for Testing Abnormality: Application to Cybersecurity \(Clarisse Boinay, Christophe Biernacki, Cristian Preda, and Thomas Anglade\)) endobj 145 0 obj << /S /GoTo /D (section*.61) >> endobj 148 0 obj (A robust co-clustering approach to short texts \(Edoardo Fibbi, Tim Verdonck, Stefan Van Aelst, Francesca Torti, and Domenico Perrotta\)) endobj 149 0 obj << /S /GoTo /D (section*.63) >> endobj 152 0 obj (Supervised Learning with Uncertain Labels for Claims Fraud Detection \(F\351lix Vandervorst, Wouter Verbeke, and Tim Verdonck\)) endobj 153 0 obj << /S /GoTo /D (section*.65) >> endobj 156 0 obj (Talk Session 2: Non-Gaussian Mixture Models 1) endobj 157 0 obj << /S /GoTo /D (section*.66) >> endobj 160 0 obj (Model-based clustering of mixed-type data via mixtures of mixed graphical models \(Michael Fop, Federica Benassi, and Giuliano Galimberti\)) endobj 161 0 obj << /S /GoTo /D (section*.68) >> endobj 164 0 obj (Mixture of Normalizing Flows for spherical density estimation \(Tin Lok James Ng and Andrew Zammit-Mangion\)) endobj 165 0 obj << /S /GoTo /D (section*.70) >> endobj 168 0 obj (Informed Finite Mixture Models \(Garritt L. Page, Maria Franco-Villoria, and Massimo Ventrucci\)) endobj 169 0 obj << /S /GoTo /D (section*.72) >> endobj 172 0 obj (Lightning Talk Session 2) endobj 173 0 obj << /S /GoTo /D (section*.73) >> endobj 176 0 obj (Oracle-LSTM: a neural network approach to mixed frequency time series prediction \(Alessandro Bitetto and Paola Cerchiello\)) endobj 177 0 obj << /S /GoTo /D (section*.76) >> endobj 180 0 obj (Fuzzy clustering of a set of dissimilarity matrices by consensus parsimonious dendrograms \(Ilaria Bombelli and Maurizio Vichi\)) endobj 181 0 obj << /S /GoTo /D (section*.78) >> endobj 184 0 obj (Relational Event Models with \040Time Varying and Random Effects: \040An Application to the Alien Species Invasions \(Martina Boschi and Ernst-Jan Camiel Wit\)) endobj 185 0 obj << /S /GoTo /D (section*.80) >> endobj 188 0 obj (A clustering model for skew-symmetric data \(Cinzia Di Nuzzo and Donatella Vicari\)) endobj 189 0 obj << /S /GoTo /D (section*.82) >> endobj 192 0 obj (SEM-Gibbs algorithm for Multilevel Cross-Classified Latent Class analysis of Binary Data \(Nicola Piras and Silvia Columbu\)) endobj 193 0 obj << /S /GoTo /D (section*.84) >> endobj 196 0 obj (Divide and Conquer: Cluster Analysis with a Different Number of Clusters per Margin \(Andrej Svetlo\235\341k, Miguel de Carvalho, Gabriel Martos Venturini, and Raffaella Calabrese\)) endobj 197 0 obj << /S /GoTo /D (section*.86) >> endobj 200 0 obj (Keynote Lecture 4) endobj 201 0 obj << /S /GoTo /D (section*.87) >> endobj 204 0 obj (Finite mixture modeling in stylometry with applications \(Volodymyr Melnykov, Shuchismita Sarkar, Xuwen Zhu, and Rong Zheng\)) endobj 205 0 obj << /S /GoTo /D (section*.89) >> endobj 208 0 obj (Talk Session 7: Non-Gaussian Mixture Models 2) endobj 209 0 obj << /S /GoTo /D (section*.90) >> endobj 212 0 obj (Innovative reparametrized beta- and gamma mixture models in a contamination setting \(Andriette Bekker, Johan Ferreira, Salvatore Daniele Tomarchio, and Antonio Punzo\)) endobj 213 0 obj << /S /GoTo /D (section*.91) >> endobj 216 0 obj (Mode Merging for Non-Gaussian Finite Mixtures \(Nam-Hwui Kim and Ryan Browne\)) endobj 217 0 obj << /S /GoTo /D (section*.93) >> endobj 220 0 obj (Partial membership models for soft clustering of multivariate count data \(Emiliano Seri, Thomas Brendan Murphy, and Roberto Rocci\)) endobj 221 0 obj << /S /GoTo /D (section*.95) >> endobj 224 0 obj (Lightning Talk Session 3) endobj 225 0 obj << /S /GoTo /D (section*.96) >> endobj 228 0 obj (The multivariate skew shifted exponential normal distributionand its use in finite mixture modeling \(Salvatore Daniele Tomarchio, Antonio Punzo, and Luca Bagnato\)) endobj 229 0 obj << /S /GoTo /D (section*.98) >> endobj 232 0 obj (Mixed-effect models with trees \(Anna Gottard, Giulia Vannucci, Leonardo Grilli, and Carla Rampichini\)) endobj 233 0 obj << /S /GoTo /D (section*.100) >> endobj 236 0 obj (Hausdorff Distance: A powerful tool for matching households and individuals in historical censuses \(Thais Pacheco Menezes, Michael Fop, and Thomas Brendan Murphy\)) endobj 237 0 obj << /S /GoTo /D (section*.101) >> endobj 240 0 obj (Machine learning algorithm to identify a metabolic profile able to predict biomarkers levels \(Fabiola Del Greco-M., Chiara Volani,, Johannes Rainer, Giuseppe Paglia, and Peter P. Pramstaller\)) endobj 241 0 obj << /S /GoTo /D (section*.103) >> endobj 244 0 obj (Clustering on Principal Components to Derive Dietary Patterns in Nutritional Epidemiology \(Andrea Maugeri, Martina Barchitta, Giuliana Favara, Claudia La Mastra, Maria Clara La Rosa, Roberta Magnano San Lio, and Antonella Agodi\)) endobj 245 0 obj << /S /GoTo /D (section*.105) >> endobj 248 0 obj (A Comparison of Classification Methods for Sequencing Data in Immunology \(Lutecia Servius, Davide Pigoli, Franca Fraternali and Joseph Chi-Fung Ng\)) endobj 249 0 obj << /S /GoTo /D (section*.107) >> endobj 252 0 obj (Talk Session 8: Latent Class Models) endobj 253 0 obj << /S /GoTo /D (section*.108) >> endobj 256 0 obj (A Latent Variable Approach to Infer Intake from Multiple Biomarker Measurements \(Silvia D'Angelo, Lorraine Brennan, and Isobel Claire Gormley\)) endobj 257 0 obj << /S /GoTo /D (section*.110) >> endobj 260 0 obj (Vine-Copula Synthetic Data Generation For Classification \(Elisabeth Griesbauer, Claudia Czado, Arnoldo Frigessi, and Ingrid Hob\346k Haff\)) endobj 261 0 obj << /S /GoTo /D (section*.112) >> endobj 264 0 obj (Spectral information criterion: reducing drastically the number of possible models \(Luca Martino, Roberto San Mill\341n-Castillo, and Eduardo Morgado\)) endobj 265 0 obj << /S /GoTo /D (section*.114) >> endobj 268 0 obj (Multiple change point clustering of count processes \(Shuchismita Sarkar and Xuwen Zhu\)) endobj 269 0 obj << /S /GoTo /D (section*.115) >> endobj 272 0 obj (Keynote Lecture 5) endobj 273 0 obj << /S /GoTo /D (section*.116) >> endobj 276 0 obj (A Bayesian two-way latent structure clustering model for genomic data integration with an application to subtyping breast cancer \(Arnoldo Frigessi\)) endobj 277 0 obj << /S /GoTo /D (section*.118) >> endobj 280 0 obj (Author Index) endobj 281 0 obj << /S /GoTo /D [282 0 R /Fit] >> endobj 288 0 obj << /Length 492 /Filter /FlateDecode >> stream xmn0~ %s{o .ZEز#SIO_JW
ˈ֤rIL9V[ٯl^~>؈ ^̀J2Jk XYqVqBXČ$bP)sX6/Hd|+}SWh)06IveӋosZ
"%Ze}mBvh]Աzw,4okxhY0ƱBp9sB`3|Kgj'O߰ DR\،I^ڮ=k)HT
xдAC|~7'(SEm
]>~Nϴ
endstream
endobj
282 0 obj
<<
/Type /Page
/Contents 288 0 R
/Resources 287 0 R
/MediaBox [0 0 595.276 841.89]
/Parent 294 0 R
/Group 284 0 R
>>
endobj
283 0 obj
<<
/Type /XObject
/Subtype /Image
/Width 1156
/Height 262
/BitsPerComponent 8
/ColorSpace /DeviceRGB
/SMask 295 0 R
/Length 923
/Filter /FlateDecode
>>
stream
x1
0 0b ` pt 2S <4
endstream
endobj
295 0 obj
<<
/Type /XObject
/Subtype /Image
/Width 1156
/Height 262
/BitsPerComponent 8
/ColorSpace /DeviceGray
/Length 97599
/Filter /FlateDecode
>>
stream
xuUfιԥCABEL DП
(!
ny\ky(pb{fϬY]O7?G_/ɠُܴI̩i7>۟\nݺ+.YyfҦa*Iptyw{ytKߕ
fzv)K𘎙T[w蓹3>m794j؏\f{1 yq?W{$_D 迷oD nfZhp-Z6?Mƥ+[!Of&]6fOXuߡcyǎ=z,//?? ȁ]-?qimTKat&
+.ԌAWH4f\qk<<y#`}.[ -%c̛ aq[axcxTfTaE{T1 ϑ)OUM ڍ]bQ\4x2m`8JaoU