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2020 – today
- 2023
[j16]Luca Scrucca
:
Entropy-Based Anomaly Detection for Gaussian Mixture Modeling. Algorithms 16(4): 195 (2023)- [j15]Luca Scrucca:
On the Influence of Data Imbalance on Supervised Gaussian Mixture Models. Algorithms 16(12): 563 (2023) - [j14]Stéphane Robin, Luca Scrucca:
Mixture-based estimation of entropy. Comput. Stat. Data Anal. 177: 107582 (2023) - 2021
- [j13]Alessandro Casa, Luca Scrucca, Giovanna Menardi:
Better than the best? Answers via model ensemble in density-based clustering. Adv. Data Anal. Classif. 15(3): 599-623 (2021) - [j12]Luca Scrucca:
A fast and efficient Modal EM algorithm for Gaussian mixtures. Stat. Anal. Data Min. 14(4): 305-314 (2021) - 2020
- [i2]Alessio Serafini, Thomas Brendan Murphy, Luca Scrucca:
Handling missing data in model-based clustering. CoRR abs/2006.02954 (2020)
2010 – 2019
- 2019
- [j11]Adrian O'Hagan, Thomas Brendan Murphy, Luca Scrucca, Isobel Claire Gormley:
Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. Comput. Stat. 34(4): 1779-1813 (2019) - [j10]Michael Fop, Thomas Brendan Murphy, Luca Scrucca:
Model-based clustering with sparse covariance matrices. Stat. Comput. 29(4): 791-819 (2019) - [i1]Luca Scrucca, Alessio Serafini:
Projection pursuit based on Gaussian mixtures and evolutionary algorithms. CoRR abs/1912.12049 (2019) - 2017
- [j9]Luca Scrucca:
On Some Extensions to GA Package: Hybrid Optimisation, Parallelisation and Islands EvolutionOn some extensions to GA package: hybrid optimisation, parallelisation and islands evolution. R J. 9(1): 187 (2017) - 2016
- [j8]Luca Scrucca:
Identifying connected components in Gaussian finite mixture models for clustering. Comput. Stat. Data Anal. 93: 5-17 (2016) - [j7]Luca Scrucca, Michael Fop, Thomas Brendan Murphy, Adrian E. Raftery:
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 8(1): 289 (2016) - 2015
- [j6]Luca Scrucca, Adrian E. Raftery:
Improved initialisation of model-based clustering using Gaussian hierarchical partitions. Adv. Data Anal. Classif. 9(4): 447-460 (2015) - 2014
- [j5]Luca Scrucca:
Graphical tools for model-based mixture discriminant analysis. Adv. Data Anal. Classif. 8(2): 147-165 (2014) - 2013
- [j4]Katherine Morris, Paul D. McNicholas, Luca Scrucca:
Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions. Adv. Data Anal. Classif. 7(3): 321-338 (2013) - 2011
- [j3]Luca Scrucca:
Model-based SIR for dimension reduction. Comput. Stat. Data Anal. 55(11): 3010-3026 (2011) - 2010
- [j2]Luca Scrucca:
Dimension reduction for model-based clustering. Stat. Comput. 20(4): 471-484 (2010)
2000 – 2009
- 2007
- [j1]Luca Scrucca:
Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression. Comput. Stat. Data Anal. 52(1): 438-451 (2007)
Coauthor Index
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