61 citations · 121 across the 15 of their papers we have counts for
16 papers
The quantile-based classifier with variable-wise parameters
Marco Berrettini, Christian Hennig, Cinzia Viroli
Quantile-based classifiers can classify high-dimensional observations by minimising a discrepancy of an observation to a class based on suitable quantiles of the within-class distr…
Dealing with overdispersion in multivariate count data
Noemi Corsini, Cinzia Viroli
The problem of overdispersion in multivariate count data is a challenging issue. Nowadays, it covers a central role mainly due to the relevance of modern technologies data, such as…
Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets
Robin Fuchs, Denys Pommeret, Cinzia Viroli
Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite of this heterogeneity,…
Directional quantile classifiers
Alessio Farcomeni, Marco Geraci, Cinzia Viroli
We introduce classifiers based on directional quantiles. We derive theoretical results for selecting optimal quantile levels given a direction, and, conversely, an optimal directio…
Classifying textual data: shallow, deep and ensemble methods
Laura Anderlucci, Lucia Guastadisegni, Cinzia Viroli
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. Th…
Quantile-based clustering
Christian Hennig, Cinzia Viroli, Laura Anderlucci
A new cluster analysis method, -quantiles clustering, is introduced. -quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd's…
Deep Gaussian Mixture Models
Cinzia Viroli, Geoffrey J. McLachlan
Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussi…
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015
Laura Anderlucci, Angela Montanari, Cinzia Viroli
In this paper we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: Annals of Statistics, B…
Infinite Mixtures of Infinite Factor Analysers
Keefe Murphy, Cinzia Viroli, Isobel Claire Gormley
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data. Typically, the numbers of clusters and latent factors must…
Bayesian Smooth-and-Match strategy for ordinary differential equations models that are linear in the parameters
Saverio Ranciati, Cinzia Viroli, Ernst Wit
In many fields of application, dynamic processes that evolve through time are well described by systems of ordinary differential equations (ODEs). The analytical solution of the OD…
Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data
Saverio Ranciati, Cinzia Viroli, Ernst Wit
Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in pri…
Modelling overdispersion heterogeneity in differential expression analysis using mixtures
Elisabetta Bonafede, Franck Picard, Stéphane Robin +1
Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using Negative Binomial dist…
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data
Laura Anderlucci, Cinzia Viroli
We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data.…
Quantile-based classifiers
Christian Hennig, Cinzia Viroli
Quantile classifiers for potentially high-dimensional data are defined by classifying an observation according to a sum of appropriately weighted component-wise distances of the co…
A factor mixture analysis model for multivariate binary data
Silvia Cagnone, Cinzia Viroli
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional…
Stochastic model selection for Mixtures of Matrix-Normals
Cinzia Viroli
Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a ma…