74 citations · 277 across the 20 of their papers we have counts for
5 papers · 1 filter
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…