74 citations · 277 across the 20 of their papers we have counts for
20 papers
A Model-Based Clustering Approach for Bounded Data Using Transformation-Based Gaussian Mixture Models
Luca Scrucca
The clustering of bounded data presents unique challenges in statistical analysis due to the constraints imposed on the data values. This paper introduces a novel method for model-…
A Model-Based Approach to Shot Charts Estimation in Basketball
Luca Scrucca, Dimitris Karlis
Shot charts in basketball analytics provide an indispensable tool for evaluating players' shooting performance by visually representing the distribution of field goal attempts acro…
Assessing uncertainty in Gaussian mixtures-based entropy estimation
Luca Scrucca
Entropy estimation plays a crucial role in various fields, such as information theory, statistical data science, and machine learning. However, traditional entropy estimation metho…
An introduction and tutorial to model-based clustering in education via Gaussian mixture modelling
Luca Scrucca, Mohammed Saqr, Sonsoles López-Pernas +1
Heterogeneity has been a hot topic in recent educational literature. Several calls have been voiced to adopt methods that capture different patterns or subgroups within students be…
Modal clustering on PPGMMGA projection subspace
Luca Scrucca
PPGMMGA is a Projection Pursuit (PP) algorithm aimed at detecting and visualizing clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained…
A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy
Luca Scrucca
Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth.Near real-time monitoring of the pandemic growth is crucial for policy makers and…
Mixture-based estimation of entropy
Stéphane Robin, Luca Scrucca
The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs be obtained…
Handling missing data in model-based clustering
Alessio Serafini, Thomas Brendan Murphy, Luca Scrucca
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missin…
A fast and efficient Modal EM algorithm for Gaussian mixtures
Luca Scrucca
In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametri…
Projection pursuit based on Gaussian mixtures and evolutionary algorithms
Luca Scrucca, Alessio Serafini
We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP i…
Better than the best? Answers via model ensemble in density-based clustering
Alessandro Casa, Luca Scrucca, Giovanna Menardi
With the recent growth in data availability and complexity, and the associated outburst of elaborate modelling approaches, model selection tools have become a lifeline, providing o…
A transformation-based approach to Gaussian mixture density estimation for bounded data
Luca Scrucca
Finite mixture of Gaussian distributions provide a flexible semi-parametric methodology for density estimation when the variables under investigation have no boundaries. However, i…
Model-based Clustering with Sparse Covariance Matrices
Michael Fop, Thomas Brendan Murphy, Luca Scrucca
Finite Gaussian mixture models are widely used for model-based clustering of continuous data. Nevertheless, since the number of model parameters scales quadratically with the numbe…
On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution
Luca Scrucca
Genetic algorithms are stochastic iterative algorithms in which a population of individuals evolve by emulating the process of biological evolution and natural selection. The R pac…
Investigation of Parameter Uncertainty in Clustering Using a Gaussian Mixture Model Via Jackknife, Bootstrap and Weighted Likelihood Bootstrap
Adrian O'Hagan, Thomas Brendan Murphy, Luca Scrucca +1
Mixture models are a popular tool in model-based clustering. Such a model is often fitted by a procedure that maximizes the likelihood, such as the EM algorithm. At convergence, th…
Model-based SIR for dimension reduction
Luca Scrucca
A new dimension reduction method based on Gaussian finite mixtures is proposed as an extension to sliced inverse regression (SIR). The model-based SIR (MSIR) approach allows the ma…
Dimension reduction for model-based clustering
Luca Scrucca
We introduce a dimension reduction method for visualizing the clustering structure obtained from a finite mixture of Gaussian densities. Information on the dimension reduction subs…
Graphical tools for model-based mixture discriminant analysis
Luca Scrucca
The paper introduces a methodology for visualizing on a dimension reduced subspace the classification structure and the geometric characteristics induced by an estimated Gaussian m…
Improved initialisation of model-based clustering using Gaussian hierarchical partitions
Luca Scrucca, Adrian E. Raftery
Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood…
clustvarsel: A Package Implementing Variable Selection for Model-based Clustering in R
Luca Scrucca, Adrian E. Raftery
Finite mixture modelling provides a framework for cluster analysis based on parsimonious Gaussian mixture models. Variable or feature selection is of particular importance in situa…