Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R, Chapman & Hall/CRC, ISBN: 978-1032234953. https://doi.org/10.1201/9781003277965
Scrucca L, Karlis D. (2024) A Model-Based Approach to Shot Charts
Estimation in Basketball. Under review.
arXiv pre-print available at https://arxiv.org/abs/2405.01182
Tiburtini M., Scrucca L. and Peruzzi L. (2024) A guide to plant
morphometrics using Gaussian Mixture Models. Under review.
bioRxiv pre-print available at
https://doi.org/10.1101/2024.04.21.590472
Scrucca L. (2023) On the influence of data imbalance on supervised Gaussian mixture models. Algorithms, 16(12), 563. https://doi.org/10.3390/a16120563
Scrucca L. (2023) Entropy-based anomaly detection for Gaussian mixture modeling. Algorithms, 16(4), 195. https://doi.org/10.3390/a16040195
Robin S., Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. https://doi.org/10.1016/j.csda.2022.107582
Santini F., Elisei L., Malmi T. and Scrucca L. (2022) Management-control-system configurations in medium-sized mechanical-engineering firms: an exploratory analysis, Accounting Research Journal, 35(6), 834–853. https://doi.org/10.1108/ARJ-06-2021-0168
Scrucca L. (2022) Modal clustering on PPGMMGA projection subspace. Australian & New Zealand Journal of Statistics, 64:2, 158–170. https://doi.org/10.1111/anzs.12360
Scrucca L. (2022) A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy. Statistical Methods & Applications, 31:4, 881–900. https://doi.org/10.1007/s10260-021-00617-y
Chirieleison C., Montrone A., Scrucca L. (2022) Destination labels for historic villages: the impact on perception, experience, and satisfaction. Tourism and Hospitality Research, 22:2, 164–179. https://doi.org/10.1177/14673584211020788
Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527
Casa A., Scrucca L., Menardi G. (2021) Better than the best? Answers via model ensemble in density-based clustering. Advances in Data Analysis and Classification, 15, 599—623. https://doi.org/10.1007/s11634-020-00423-6
Chirieleison C., Montrone A., Scrucca L. (2020) Event sustainability and sustainable transportation: a positive reciprocal influence. Journal of Sustainable Tourism, 28:2, 240–262. https://doi.org/10.1080/09669582.2019.1607361
Scrucca L., Serafini A. (2019) Projection pursuit based on Gaussian mixtures and evolutionary algorithms. Journal of Computational and Graphical Statistics, 28:4, 847–860. https://doi.org/10.1080/10618600.2019.1598871
O’Hagan A., Murphy T. B., Scrucca L. and Gormley I. C. (2019) Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. Computational Statistics, 34, 1779—1813. https://doi.org/10.1007/s00180-019-00897-9
Fop M., Murphy T. B., Scrucca L. (2019) Model-based clustering with sparse covariance matrices. Statistics and Computing, 29:4, 791–819. https://doi.org/10.1007/s11222-018-9838-y
Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data. Biometrical Journal, 61:4, 873–888. https://doi.org/10.1002/bimj.201800174
Ndabashinze B., Ustundag Siray G., Scrucca L. (2019) Genetic algorithms applied to fractional polynomials for power selection: application to diabetes data, Turkish Journal of Forecasting, 3, 15–25. https://doi.org/10.34110/forecasting.514761
Giustozzi M., Vedovati M. C., Verso M., Scrucca L., Conti S., Verdecchia P., Bogliari G., Pierpaoli L., Agnelli G. and Becattini C. (2019) Patients aged 90 years or older with atrial fibrillation treated with oral anticoagulants: A multicentre observational study. International Journal of Cardiology, 281, 56–61. https://doi.org/10.1016/j.ijcard.2019.01.071
Giustozzi M., Vedovati M. C., Verso M., Scrucca L., Conti S., Verdecchia P., Bogliari G., Pierpaoli L., Agnelli G. and Becattini C. (2019) Data on the use of oral anticoagulants in nonagenarians with atrial fibrillation, Data in Brief, Vol. 23, 103794. https://doi.org/10.1016/j.dib.2019.103794
Chirieleison C., Montrone A., Scrucca L. (2018) Eventi e mobilità sostenibile: un modello basato sulla cluster analysis. Economia della Cultura, anno XXVIII, 2018, n. 4.
Scrucca L. and Raftery A. E. (2018) clustvarsel: A Package Implementing Variable Selection for Gaussian Model-based Clustering in R. Journal of Statistical Software, 84:1, 1–28. http://doi.org/10.18637/jss.v084.i01
Chirieleison C., Montrone A., Scrucca L. (2017) La mobilità sostenibile durante i grandi eventi italiani: un’analisi con il model-based clustering approach. Turistica, anno XXVI, vol. 4, 35–55.
Chirieleison C. and Scrucca L. (2017) Event sustainability and transportation policy: a model-based cluster analysis for a cross-comparison of hallmark events. Tourism Management Perspectives, 24, 72–85. https://doi.org/10.1016/j.tmp.2017.07.020
Chirieleison C. and Scrucca L. (2017) Shaping students' attitudes towards business ethics and corporate social responsibility: education versus personal perspectives, Education, 7:5, 83–95. http://article.sapub.org/10.5923.j.edu.20170705.01.html
Scrucca L. (2017) On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution, The R Journal, 9:1, 187–206. https://doi.org/10.32614/RJ-2017-008
Parisi A., Scrucca L., Desiderio J., Gemini A., Guarino S., Ricci F., Cirocchi R., Palazzini G., D’Andrea V., Minelli L., Trastulli S. (2017) Robotic right hemicolectomy: Analysis of 108 consecutive procedures and multidimensional assessment of the learning curve, Surgical Oncology, 26:1, 28–36. http://doi.org/10.1016/j.suronc.2016.12.005
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8:1, 205–233. https://doi.org/10.32614/RJ-2016-021
Scrucca L. (2016) Identifying connected components in Gaussian finite mixture models for clustering. Computational Statistics & Data Analysis. 93, 5–17. http://dx.doi.org/10.1016/j.csda.2015.01.006
Sahdra B., Ciarrochi J., Parker P. and Scrucca L. (2016) Using genetic algorithms in a large nationally representative American sample to abbreviate the Multidimensional Experiential Avoidance Questionnaire. Frontiers in Psychology, 7:189, 1–14. https://doi.org/10.3389/fpsyg.2016.00189
Scrucca L. and Raftery A. E. (2015) Improved initialisation of model-based clustering using Gaussian hierarchical partitions. Advances in Data Analysis and Classification, 9:4, 447–460. https://doi.org/10.1007/s11634-015-0220-z
Cencetti C., de Rosa P., Fredduzzi A., Minelli A., and Scrucca L. (2015) A statistical test for drainage network recognition using MeanStreamDrop analysis. Geomatics, Natural Hazards and Risk, 5–7, 534–553. https://doi.org/10.1080/19475705.2014.897655
Scrucca L. (2014) Graphical tools for model-based mixture discriminant analysis. Advances in Data Analysis and Classification, 8:2, 147–165. https://doi.org/10.1007/s11634-013-0147-1
Morris K., McNicholas P., and Scrucca L. (2013) Dimension reduction for model-based clustering via mixtures of multivariate t-distributions. Advances in Data Analysis and Classification, 7:3, 321–338. https://doi.org/10.1007/s11634-013-0137-3
Scrucca L. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 5:4, 1–37. http://www.jstatsoft.org/v53/i04
Chirieleison C., Montrone A., Scrucca L. (2013) Measuring the impact of a profit oriented event on tourism: the Eurochocolate festival in Perugia, Italy. Tourism Economics, 19:6, 1411–1428. https://doi.org/10.5367%2Fte.2013.0269
Chirieleison C., Montrone A., Scrucca L. (2012) L’impatto degli eventi sul territorio: il caso Eurochocolate. AUR&S, 7-8:12, 133–156.
Scrucca L. (2011) Model-based SIR for dimension reduction. Computational Statistics & Data Analysis, 55:11, 3010–3026. https://doi.org/10.1016/j.csda.2011.05.006
Scrucca L. (2010) Dimension Reduction for Model-Based Clustering. Statistics and Computing, 20:4, 471–484. https://doi.org/10.1007/s11222-009-9138-7
Scrucca L., Santucci A., Aversa F. (2010) Regression Modeling of Competing Risk Using R: An In Depth Guide for Clinicians. Bone Marrow Transplantation, 45, 1388–1395. https://doi.org/10.1038/bmt.2009.359
Scrucca L. (2007) Class prediction and gene selection for DNA microarrays using sliced inverse regression. Computational Statistics & Data Analysis, 52, 438–451. https://doi.org/10.1016/j.csda.2007.02.005
Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis using R: an easy guide for clinicians. Bone Marrow Transplantation (2007) 40, 381–387. https://doi.org/10.1038/sj.bmt.1705727
Scrucca L. (2004) L’identificazione dei distretti industriali e delle aree di sviluppo economico omogenee e/o integrate. Rivista dell’Agenzia Umbria Ricerche, 1, 71–90.
Scrucca L. (2004) qcc: an R package for quality control charting and statistical process control. R News, 4:1, June 2004, 11-17. https://cran.r-project.org/doc/Rnews/Rnews_2004-1.pdf
Scrucca L., Weisberg S. (2004) A simulation study to investigate the behavior of the log-density ratio under normality. Communications in Statistics (simulation and computation), 33:1, 159–178. https://doi.org/10.1081/SAC-120028439
De Angelis M., Scrucca L., Leandri M., Mincigrucci S., Bistoni S., Bovi M., Calabrese G., Pippi R., Parretti D., Grilli P., Colorio P., Fattorini M., Flamini O., Pacetti E., Travaglini A., Santeusanio F. (2003) Prevalence of carotid stenosis in type 2 diabetic patients asymptomatic for cerebrovascular disease. Diabetes, Nutrition & Metabolism, 16:1, 48–55.
Scrucca L. (2002) Graphics for studying logistic regression models. Statistical Methods & Applications, 11:3, 371–394. https://doi.org/10.1007/BF02509833
Scrucca L. (2001) Nonparametric kernel smoothing methods. The sm library for Xlisp-Stat. Journal of Statistical Software, 6. http://www.jstatsoft.org/v06/i07
Scrucca L. (2001) A review and computer code for assessing the structural dimension of a regression model: uncorrelated 2D views. Computational Statistics & Data Analysis, 36:2, 163–177. https://doi.org/10.1016/S0167-9473(00)00035-9
Scrucca L. (2000) Assessing multivariate normality through interactive dynamic graphics. Quaderni di Statistica, 2, 221–240.
Scrucca (1996) I sondaggi elettorali nella recente esperienza italiana. Induzioni, n. 12, 59–81.
Serafini A., Scrucca L., Alfò M., Giordani P., Ferraro M. B. (2023) Fuzzy and model based clustering methods: can we fruitfully compare them? In: Brentari, E., Chiodi, M., Wit, EJ.C. (eds) Models for Data Analysis. SIS 2018, 49th Meeting of Italian Statistical Society, Palermo, Italy, June 20–22. Springer Proceedings in Mathematics & Statistics, pp. 283–304.
Scrucca L. (2016) Genetic algorithms for subset selection in model-based clustering. In Unsupervised Learning Algorithms, Celebi M. E. and Aydin K. (editors), Springer International Publishing, pp. 55–70.
Chirieleison C., Scrucca L. (2016) CSR education in Italy. In Social Responsibility Education Across Europe. A Comparative Approach, Turker D., Altuntas Vural C., Idowu S. O. (editors), Springer International Publishing, pp. 139–159.
Scrucca L., Bar-Hen A. (2013) A model-based dimension reduction approach to classification of gene expression data. In Advances in Theoretical and Applied Statistics, Torelli N., Pesarin F., Bar-Hen A. (editors), Springer, pp. 221–230.
Scrucca L. (2011) A Geometric Approach to Subset Selection and Sparse Sufficient Dimension Reduction. In New Perspectives in Statistical Modeling and Data Analysis, editors Ingrassia S., Rocci R., Vichi M., Springer-Verlag, Berlin Heidelberg, pp. 569–576.
Bartolucci F., Scrucca L. (2010) Point Estimation Methods with Applications to Item Response Theory Models. In International Encyclopedia of Education, Penelope Peterson, Eva Baker, Barry McGaw (editors), 3rd edition, Oxford: Elsevier, Vol. 7, pp. 366–373.
Scrucca L. (2010) Visualization of model-based clustering structures. In Data Analysis and Classification, editors Palumbo F., Lauro C., Greenacre M., Berlin, Springer-Verlag, pp. 67–75.
Scrucca L. (2006) Regularized sliced inverse regression with applications in classification. In Data Analysis, Classification and the Forward Search, editors Zani S., Cerioli A., Riani M., Vichi M., Berlin, Springer-Verlag, pp. 59–66.
Scrucca L. (2005) La metodologia utilizzata per l’identificazione di aggregazioni sub-regionali omogenee. In Verso una rilettura dei sistemi locali dell’Umbria: prime applicazioni di una nuova metodologia di clusterizzazione, Quaderni PRASSI - Agenzia Umbria Ricerche, Cap. 2.
Scrucca L. (2003) Aree di sviluppo omogenee e/o integrate. Approcci e metodologie. Paper dell’Agenzia Umbria Ricerche, Collana: sviluppo e locale, n. 8, pp. 1–47.
Scrucca, L. (2023) Gaussian Mixture Models for Probabilistic Learning, From Physics to Medicine: eXplainable AI workshop, Milano, November 20–22, 2023.
Scrucca, L. (2023) Anomaly Detection of Financial Ratios using Gaussian Mixture Models, Workshop Annuale del Dipartimento di Economia 2023 (WADE 2023), Department of Economics, 30–31 October 2023 (Session II 31-10, Part 2.1.8), Zenodo. https://doi.org/10.5281/zenodo.10053896
Scrucca L. (2023) Mixture-based Estimation of Entropy and Its Applications, Working Group on Model-Based Clustering. Carnegie Mellon University, Pittsburgh (US), July 17–21, 2023.
Scrucca L. (2022) Gaussian mixtures using range-logit transformation with an application to image segmentation. MBC2 Book of Abstracts, 6th International Workshop on Models and Learning for Clustering and Classification, Catania, 31 August – 2 September 2022, pp. 11–12.
Scrucca L. (2022) Silhouette Analysis for Model-Based Clustering, ECDA 2022, European Conference on Data Analysis, Naples, Italy, September 14–16 2022.
Scrucca L. (2021) Stacking ensemble of Gaussian mixtures for classification, Working Group on Model-Based Clustering. Athens University of Economics and Business, Athens, October 25–30, 2021.
Scrucca L. (2021) Stacking ensemble learning with Gaussian mixtures. In CLADAG 2021 Book of Abstracts and Short Papers. 13th Scientific Meeting of the Classification and Data Analysis Group (editors Giovanni C. Porzio, Carla Rampichini, Chiara Bocci), Firenze University Press, pp. 420–423. Firenze, September 9–11 2021.
Scrucca L. (2021) Statistical models for infectious diseases epidemiology, COST Action: CA18208 - Novel tools for test evaluation and disease prevalence estimation, Training School, Perugia, September 16–17 2021.
Scrucca L., Serafini A. (2019) Mutual information Estimation via Gaussian Mixture Modelling, Working Group on Model-Based Clustering. WU Vienna University of Economics and Business, Vienna, July 14–20, 2019.
Scrucca L. (2019) A fast and efficient Modal EM algorithm for Gaussian mixtures. In CLADAG 2019 Book of Short Papers. 12th Scientific Meeting of the Classification and Data Analysis Group (editors Giovanni C. Porzio, Francesca Greselin, Simona Balzano), Edizioni Università di Cassino, pp. 432–435. Cassino, September 11–13 2019.
Casa A., Scrucca L., Menardi G. (2018) Averaging via Stacking in Model-based Clustering, MBC2 – Workshop on Model Based Clustering and Classification. Catania, 5–7 September, 2018.
Alfò M., Ferraro M.B, Giordani P., Scrucca L., Serafini A. (2018) Model-based clustering vs Fuzzy clustering: a simulation comparison, Working Group on Model-Based Clustering. University of Michigan, Ann Arbor (USA), July 15–21, 2018.
Alfò M., Ferraro M.B, Giordani P., Scrucca L., Serafini A. (2018) A Comparison of Model-Based and Fuzzy Clustering Methods. SIS2018, 49th Scientific Meeting of the Italian Statistical Society. Palermo, 20–22 June, 2018. Abbruzzo A., Brentari E., Chiodi M., Piacentino D. (editors), Book of Short Papers SIS 2018, Pearson, pp. 208–215.
Casa A., Scrucca L., Menardi G. (2018) On the Selection Uncertainty in Parametric Clustering, ECDA 2018, European Conference on Data Analysis, Paderborn, Germany, July 04–06 2018, Book of Abstracts, pp. 22–23.
Piersanti A., Tavoloni T., Scrucca L., Galarini R. (2018) Validation Data Processing by ADVeRSE 2.0 Free Software Package: Assessment of the Analytical Requirements of a GC-MS Method for the Determination of 16 PAHs in Smoked Fish, XXII International Mass Spectrometry Conference (IMSC), 26–31 August 2018, Florence.
Fop M., Murphy T.B., Scrucca L. (2017) Model-based Clustering with Sparse Covariance Matrices. SIS 2017. Statistics and Data Science: new challenges, new generations. Florence, 28–30 June, 2017. Petrucci A., Verde R. (editors), Proceedings of the Conference of the Italian Statistical Society, pp. 437–440.
Scrucca L., Fop M., Murphy T.B., Raftery A.E. (2017) The mclust R package for clustering, classification and density estimation using Gaussian finite mixture models, 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, 11th International Conference on Computational and Financial Econometrics, Senate House, University of London, UK, December 16-18 2017, Book of Abstracts, p. 175.
Scrucca L. (2017) Boosting Gaussian mixture models for classification, Working Group on Model-Based Clustering. Università degli Studi di Perugia, Perugia, July 17—21, 2017.
Scrucca L. (2016) On some recent improvements to mixture modelling estimation: Gaussian hierarchical partitions based on data transformations and evolutionary EM, Working Group on Model-Based Clustering. Université Paris Descartes, Paris, July 17–23, 2016.
Scrucca L. (2016) Poisson change-point models estimated by genetic algorithms. In SIS2016, 48th Scientific Meeting of the Italian Statistical Society. Salerno, 8–10 June, 2016.
Scrucca L. (2015) A quick tour of new functionalities in MCLUST version 5, Working Group on Model-Based Clustering. University of Washington, Seattle, July 19–25, 2015.
Galarini R., Moretti S., Romanelli S., Scrucca L. (2015) Informatics tools to manage validation data of multiresidue analytical methods. MASSA 2015, Alghero (Italy), June 2015.
Scrucca L. (2014) Identifying connected components in Gaussian finite mixture models for clustering, Workshop on “Clustering methods and their applications”. Free University of Bozen - Bolzano, November 28th, 2014.
Scrucca L. (2014) Model-based density estimation for bounded data, Working Group on Model-Based Clustering. University College Dublin, Dublin, July 20–26, 2014.
Scrucca L., Raftery A. E. (2014) Improved initialisation of model-based clustering using a Gaussian hierarchical partition, MBC2 – Workshop on Model Based Clustering and Classification. Catania, 3–5 September, 2014.
Scrucca L. (2013) Identifying connected components in model-based clustering, Working Group on Model-Based Clustering. Università di Bologna, July 21–27, 2013.
Scrucca L. (2013) On the implementation of a parallel algorithm for variable selection in model-based clustering. CLADAG 2013. 9th Scientific Meeting of the Classification and Data Analysis Group. Book of Abstracts (editors Tommaso Minerva, Isabella Morlini, Francesco Palumbo). Cleup. 9th Meeting of the CLassification and Data Analysis Group of the Italian Statistical Society, Modena, 18–20 September 2013.
Scrucca L. (2012) MCLUST version 4 session, Working Group on Model-Based Clustering. University of Guelph, July 16–20, 2012.
Scrucca L. (2012) Graphical tools for model-based mixture discriminant analysis, MBC2 – Workshop on Model Based Clustering and Classification, Catania, 6–7 September 2012
Scrucca L. (2011) Genetic algorithms for subset selection in model-based clustering, Working Group on Model-Based Clustering, University of Glasgow, Glasgow, July 17–23, 2011.
Scrucca L. (2011) Classification on a dimension reduced subspace. CLADAG 2011, Book of Abstracts (editors Paola Cerchiello, Claudia Tarantola), Pavia University Press, p. 47. 8th Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society, Pavia, 7–9 September 2011.
Scrucca L. (2010) Some recent advances on dimension reduction for high-dimensional data. In GfKL-CLADAG 2010, Book of Abstracts, pp. 99–100. Joint meeting GfKl-CLADAG 2010, 8–10 September 2010.
Scrucca L., Bar-Hen A. (2010) A model-based dimension reduction approach to classification of gene expression data. In SIS2010, Atti della XLV Riunione Scientifica della Società Italiana di Statistica, CLEUP. Padova, 16–18 June, 2010.
Scrucca L. (2010) Genetic algorithms for subset selection in model-based clustering. In SIS2010, Atti della XLV Riunione Scientifica della Società Italiana di Statistica CLEUP. Padova, 16-18 June, 2010.
G. Saluti, Galarini R., Scrucca L. (2010) Multivariate Charts for Quality Control of Multiresidue Analytical Methods. In Sixth International Symposium on Hormone and Veterinary Drug Residue Analysis, Ghent (Belgium) 1-4 June 2010.
Scrucca L. (2009) A Finite Mixture Model Approach to Sliced Inverse Regression, Working Group on Model-Based Clustering. Institut Henri Poincaré, Paris, July 19–25, 2009.
Scrucca L. (2009) A Geometric Approach to Subset Selection and Sparse Sufficient Dimension Reduction. In CLADAG 2009, Book of Short Papers (editors Ingrassia S. e Rocci R.), Padova, CLEUP, pp. 235–238. VII Riunione Scientifica del Gruppo di Classificazione e Analisi dei Dati della Società Italiana di Statistica, Catania, 9–11 September 2009.
Scrucca L. (2008) A mixture model approach to dimension reduction in regression (Un approccio basato sui modelli mistura per la riduzione della dimensionalità nell’ambito della regressione). In SIS2008, Atti della XLIV Riunione Scientifica della Società Italiana di Statistica, pp. 233–240. Arcavacata di Rende, 25–27 June, 2008.
Scrucca L. (2007) Dimension reduction and visualization for model-based clustering, Working Group on Model-Based Clustering. Trinity College Dublin, Dublin, July 16–20, 2007.
Scrucca L. (2007) Visualization of model-based clustering structures. In CLADAG 2007, Book of Short papers, Lauro C. e Palumbo F. (editors), pp. 451–454. VI Riunione Scientifica del Gruppo di Classificazione e Analisi dei Dati della Società Italiana di Statistica, Macerata 12-14 September 2007.
Scrucca L. (2006) Dimension reduction and visualization for model-based clustering. In COMPSTAT 2006, Book of abstracts, 17th Symposium of IASC on Computational Statistics, Rome, 28 Agust - 1 September 2006.
Piersanti A., Galarini R., Tavoloni T., Scrucca L. (2006) Polychlorobiphenyl (18 congeners) in mussels from middle adriatic sea. In Proceedings of 26th International Symposium on Halogenated Persistent Organic Pollutants - DIOXIN 2006, Oslo, 21-25 August 2006, pp. 1951–1954. Organohalogen Compounds Vol. 68.
Scrucca L. (2005) Regularized sliced inverse regression with applications in classification. In CLADAG 2005, Book of Short papers (editors Zani S. e Cerioli A.), pp. 509–512. V Riunione Scientifica del Gruppo di Classificazione e Analisi dei Dati della Società Italiana di Statistica, Parma 6-8 giugno 2005.
Scrucca L. (2004) Class prediction and gene selection for DNA microarrays using sliced inverse regression. In Proceedings of the 19th International Workshop on Statistical Modelling (IWSM 2004), Firenze, 4-8 July 2004, pp. 259–263.
Scrucca L. (2003) On a test procedure for assessing a parametric approximation to a nonlinear term in semiparametric models. In Proceedings ISI 2003 - International Statistical Institute (13-20 August 2003, Berlin, Germany), Contributed Papers, Vol. LX, Book 2, pp. 397–398.
Scrucca L. (2002) Robust sliced inverse regression and outliers detection. In Euroworkshop on Statistical Modelling Model Building and Evaluation, Bernried, Germany, Oct 31-Nov 3 2002.
Porzio G.C., Scrucca L. (2002) Visualizing the correlation coefficient (Il coefficiente di correlazione: una sua rappresentazione grafica). In SIS2002, Atti della XLI Riunione Scientifica della Società Italiana di Statistica, Milano, 5-7 Giugno, 2002.
Attygalle D., Francis B., Scrucca L. (2002) Model coverage, forward searching and multiple outlier detection. In 17th International Workshop on Statistical Modelling. Crete, Greece, 8-12 July, 2002.
Ranalli M.G., Scrucca L. (2002) Understanding influential observations in regression estimation of finite population means using graphs. In COMPSTAT 2002, Book of abstracts, 15th Symposium of IASC on Computational Statistics, Berlin, Germany, 24-28 August 2002.
Scrucca L. (2001) Visualizing and assessing partial correlation coefficients through added variable plots. In Atti del Convegno S.Co. 2001, Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, Bressanone, 24-26 Settembre 2001, a cura di C. Provasi, Cleup Editrice, Padova.
Scrucca L. (1999) Dynamic probability plot for assessing multivariate normality. In Atti del Convegno S.Co. 1999, Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione, Venezia, 27-29 Settembre 1999, pp. 122–129.
Scrucca L., Raftery A. E. (2014) clustvarsel: A Package Implementing Variable Selection for Model-based Clustering in R. Technical Report, Department of Statistics, University of Washington, No. 629, pp. 1–27.
Fraley C., Raftery A. E., Murphy T. B., Scrucca L. (2012) mclust version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report, Department of Statistics, University of Washington, No. 597, pp. 1–58
Scrucca L. (2006) Subset selection in dimension reduction methods. Quaderni del Dipartimento di Economia, Finanza e Statistica, Università degli Studi di Perugia, n. 23, Settembre 2006.
Scrucca L. (2005) Clustering multivariate spatial data based on local measures of spatial autocorrelation. An application to the labour market of Umbria. Quaderni del Dipartimento di Economia, Finanza e Statistica, Università degli Studi di Perugia, n. 20, Ottobre 2005.
Scrucca L. (2022) Pandemia e previsioni: per l’Umbria una curva in discesa. AUR Focus. https://www.agenziaumbriaricerche.it/focus/pandemia-e-previsioni-per-lumbria-una-curva-in-discesa/
Scrucca L. (2021) Incidenza e impatto sul settore ospedaliero della pandemia CoViD-19: un confronto tra regioni. AUR Focus. https://www.agenziaumbriaricerche.it/focus/incidenza-e-impatto-sul-settore-ospedaliero-della-pandemia-covid-19-un-confronto-tra-regioni/
Serafini A., Murphy T. B., Scrucca L. (2020) Handling missing data in model-based clustering. arXiv:2006.02954 pre-print available at https://arxiv.org/abs/2006.02954