Luca Scrucca

Personal Details

First Name:	Luca
Middle Name:
Last Name:	Scrucca
Suffix:
RePEc Short-ID:	psc972
	[This author has chosen not to make the email address public]
	https://luca-scr.github.io
Bluesky:	@luca-scr.bsky.social

Affiliation

Dipartimento di Scienze Statistiche "Paolo Fortunati"
Alma Mater Studiorum - Università di Bologna

Bologna, Italy
http://www.stat.unibo.it/
RePEc:edi:dsbolit (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Luca Scrucca, 2005. "Clustering multivariate spatial data based on local measures of spatial autocorrelation," Quaderni del Dipartimento di Economia, Finanza e Statistica 20/2005, Università di Perugia, Dipartimento Economia.

   Cited by:

   1. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
   2. Davide Castellani & Fabio Pieri, 2011. "Foreign Investments and Productivity Evidence from European Regions," Quaderni del Dipartimento di Economia, Finanza e Statistica 83/2011, Università di Perugia, Dipartimento Economia.
   3. Francesco Venturini, 2011. "Product variety, product quality, and evidence of Schumpeterian endogenous growth: a note," Quaderni del Dipartimento di Economia, Finanza e Statistica 93/2011, Università di Perugia, Dipartimento Economia.
   4. Stefano Herzel & Marco Nicolosi & Cătălin Stărică, 2012. "The cost of sustainability in optimal portfolio decisions," The European Journal of Finance, Taylor & Francis Journals, vol. 18(3-4), pages 333-349, May.
      • Stefano Herzel, Stefano & Marco Nicolosi, Marco & Starica, Catalin, 2010. "The cost of sustainability on optimal portfolio choices," Sustainable Investment and Corporate Governance Working Papers 2010/15, Sustainable Investment Research Platform.
      • Stefano Herzel & Marco Nicolosi & Catalin Starica, 2011. "The cost of sustainability on optimal portfolio choices," Quaderni del Dipartimento di Economia, Finanza e Statistica 84/2011, Università di Perugia, Dipartimento Economia.
   5. Mirella Damiani & Fabrizio Pompei & Andrea Ricci, 2011. "Temporary job protection and productivity growth in EU economies," Quaderni del Dipartimento di Economia, Finanza e Statistica 87/2011, Università di Perugia, Dipartimento Economia.
      • Damiani, Mirella & Pompei, Fabrizio & Ricci, Andrea, 2011. "Temporary job protection and productivity growth in EU economies," MPRA Paper 29698, University Library of Munich, Germany.
   6. Mirella Damiani, 2010. "Labour regulation, corporate governance and varieties of capitalism," Quaderni del Dipartimento di Economia, Finanza e Statistica 76/2010, Università di Perugia, Dipartimento Economia.
   7. Silvia Micheli, 2010. "Learning Curve and Wind Power," Quaderni del Dipartimento di Economia, Finanza e Statistica 81/2010, Università di Perugia, Dipartimento Economia.

Articles

1. Robin, Stéphane & Scrucca, Luca, 2023. "Mixture-based estimation of entropy," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

   Cited by:

   1. Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2022. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

2. Luca Scrucca, 2022. "A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 881-900, October.

   Cited by:

   1. G. Alleva & G. Arbia & P. D. Falorsi & V. Nardelli & A. Zuliani, 2023. "Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 983-999, September.

3. Fabio Santini & Luca Elisei & Teemu Malmi & Luca Scrucca, 2022. "Management-control-system configurations in medium-sized mechanical-engineering firms: an exploratory analysis," Accounting Research Journal, Emerald Group Publishing Limited, vol. 35(6), pages 834-853, November.

   Cited by:

   1. Federica Palazzi & Francesca Sgrò & Massimo Ciambotti & Nick Bontis & Lorenzo Gelsomini, 2023. "The moderating effect of corporate size on the relationship between prospector strategy and management accounting practices," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 34(2), pages 135-165, June.
   2. Valeria Vannoni & Federica Palazzi & Annalisa Sentuti & Francesca Sgrò, 2024. "The Role of the Management Control System in Supporting ESG-Focused Transformation in Financial Intermediaries: A Case Study of an Italian Bank," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 16(1), pages 1-22, January.

4. Alessandro Casa & Luca Scrucca & Giovanna Menardi, 2021. "Better than the best? Answers via model ensemble in density-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 599-623, September.

   Cited by:

   1. Jose Ameijeiras-Alonso & Jochen Einbeck, 2024. "A fresh look at mean-shift based modal clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 1067-1095, December.
   2. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.

5. Adrian O’Hagan & Thomas Brendan Murphy & Luca Scrucca & Isobel Claire Gormley, 2019. "Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap," Computational Statistics, Springer, vol. 34(4), pages 1779-1813, December.

   Cited by:

   1. Rasmus Lentz & Suphanit Piyapromdee & Jean‐Marc Robin, 2023. "The Anatomy of Sorting—Evidence From Danish Data," Econometrica, Econometric Society, vol. 91(6), pages 2409-2455, November.
   2. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Model-based Clustering of Count Processes," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 188-211, July.
   3. Keefe Murphy & T. Brendan Murphy & Raffaella Piccarreta & I. Claire Gormley, 2021. "Clustering longitudinal life‐course sequences using mixtures of exponential‐distance models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1414-1451, October.
   4. Oliver Cassagneau-Francis & Robert Gary-Bobo & Julie Pernaudet & Jean-Marc Robin, 2022. "A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03869547, HAL.
      • Oliver Cassagneau-Francis & Robert Gary-Bobo & Julie Pernaudet & Jean-Marc Robin, 2022. "A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages," SciencePo Working papers Main hal-03869547, HAL.
      • Oliver Cassagneau-Francis & Robert Gary-Bobo & Julie Pernaudet & Jean-Marc Robin, 2022. "A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages," Working Papers hal-03869547, HAL.
   5. Oliver Cassagneau-Francis, 2022. "Revisiting the Returns to Higher Education: Heterogeneity by Cognitive and Non-Cognitive Abilities," Working Papers hal-04067399, HAL.
      • Oliver Cassagneau-Francis, 2022. "Revisiting the Returns to Higher Education: Heterogeneity by Cognitive and Non-Cognitive Abilities," SciencePo Working papers Main hal-04067399, HAL.

6. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.

   Cited by:

   1. José E. Chacón, 2019. "Mixture model modal clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 379-404, June.
   2. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
   3. José E. Chacón, 2020. "The Modal Age of Statistics," International Statistical Review, International Statistical Institute, vol. 88(1), pages 122-141, April.
   4. Abby Flynt & Nema Dean, 2019. "Growth Mixture Modeling with Measurement Selection," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 3-25, April.
   5. Kunhui Zhang & Yen-Chi Chen, 2021. "Refined Mode-Clustering via the Gradient of Slope," Stats, MDPI, vol. 4(2), pages 1-23, June.
   6. Alessandro Casa & Luca Scrucca & Giovanna Menardi, 2021. "Better than the best? Answers via model ensemble in density-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 599-623, September.
   7. Warren C Jochem & Douglas R Leasure & Oliver Pannell & Heather R Chamberlain & Patricia Jones & Andrew J Tatem, 2021. "Classifying settlement types from multi-scale spatial patterns of building footprints," Environment and Planning B, , vol. 48(5), pages 1161-1179, June.

7. Luca Scrucca & Adrian Raftery, 2015. "Improved initialisation of model-based clustering using Gaussian hierarchical partitions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 447-460, December.

   Cited by:

   1. Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 485-512, June.
   2. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 493-518, September.
   3. Michael Fop & Pierre-Alexandre Mattei & Charles Bouveyron & Thomas Brendan Murphy, 2022. "Unobserved classes and extra variables in high-dimensional discriminant analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 55-92, March.
   4. Alessandro Casa & Luca Scrucca & Giovanna Menardi, 2021. "Better than the best? Answers via model ensemble in density-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 599-623, September.
   5. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
   6. Branislav Panić & Jernej Klemenc & Marko Nagode, 2020. "Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation," Mathematics, MDPI, vol. 8(7), pages 1-30, July.
   7. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.

8. Luca Scrucca, 2014. "Graphical tools for model-based mixture discriminant analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 147-165, June.

   Cited by:

   1. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
   2. Julio César Hernández-Sánchez & José Luis Vicente-Villardón, 2017. "Logistic biplot for nominal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(2), pages 307-326, June.
   3. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
   4. Nicola Loperfido, 2019. "Finite mixtures, projection pursuit and tensor rank: a triangulation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 145-173, March.
   5. Jose Giovany Babativa-Márquez & José Luis Vicente-Villardón, 2021. "Logistic Biplot by Conjugate Gradient Algorithms and Iterated SVD," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
   6. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.

9. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).

   Cited by:

   1. Shi, Xingjie & Huang, Yuan & Huang, Jian & Ma, Shuangge, 2018. "A Forward and Backward Stagewise algorithm for nonconvex loss functions with adaptive Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 235-251.
   2. Finn Olesen, 1999. "Monetær integration i EU," Working Papers 2/99, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.
   3. Gabriele Perrone & Gabriele Soffritti, 2024. "Parsimonious Seemingly Unrelated Contaminated Normal Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 533-567, November.
   4. Nikolaos Nagkoulis & Konstantinos L. Katsifarakis, 2022. "Using Game Theory to Assign Groundwater Pumping Schedules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1571-1586, March.
   5. Franco, Manuel & Vivo, Juana-Maria & Kundu, Debasis, 2020. "A generalized Freund bivariate model for a two-component load sharing system," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
   6. Muhammet Burak Kılıç & Yusuf Şahin & Melih Burak Koca, 2021. "Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation," Computational Statistics, Springer, vol. 36(2), pages 1219-1242, June.
   7. Gerard Mor & Jordi Cipriano & Eloi Gabaldon & Benedetto Grillone & Mariano Tur & Daniel Chemisana, 2021. "Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems," Energies, MDPI, vol. 14(17), pages 1-25, September.
   8. Michael B. Devereux & Charles Engel & Giovanni Lombardo, 2020. "Implementable Rules for International Monetary Policy Coordination," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 68(1), pages 108-162, March.
   9. Shukla, Abhinek & Grover, Rhythm & Kundu, Debasis & Mitra, Amit, 2022. "Approximate least squares estimators of a two-dimensional chirp model," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
   10. Teodora Basile & Antonio Maria Amendolagine & Luigi Tarricone, 2022. "Rootstock’s and Cover-Crops’ Influence on Grape: A NIR-Based ANN Classification Model," Agriculture, MDPI, vol. 13(1), pages 1-11, December.
   11. Thomas Grubinger & Achim Zeileis & Karl-Peter Pfeiffer, 2011. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Working Papers 2011-20, Faculty of Economics and Statistics, Universität Innsbruck.
       • Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
   12. Yu, Huan & Yang, Jun & Peng, Rui & Zhao, Yu, 2016. "Reliability evaluation of linear multi-state consecutively-connected systems constrained by m consecutive and n total gaps," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 35-43.
   13. Francesco Dotto & Alessio Farcomeni & Maria Grazia Pittau & Roberto Zelli, 2019. "A dynamic inhomogeneous latent state model for measuring material deprivation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 495-516, February.
   14. Fabio Blasutto & David de la Croix, 2022. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," LIDAM Discussion Papers IRES 2022011, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
       • Fabio Blasutto & David de la Croix, 2023. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," ULB Institutional Repository 2013/364712, ULB -- Universite Libre de Bruxelles.
       • Fabio Blasutto & David de la Croix, 2023. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," The Economic Journal, Royal Economic Society, vol. 133(656), pages 2899-2924.
       • de la Croix, David & Blasutto, Fabio, 2021. "Catholic Censorship and the Demise of Knowledge Production in Early Modern Italy," CEPR Discussion Papers 16409, C.E.P.R. Discussion Papers.
   15. Huan Yu & Jun Yang & Yu Zhao, 2018. "Reliability of nonrepairable phased-mission systems with common bus performance sharing," Journal of Risk and Reliability, , vol. 232(6), pages 647-660, December.
   16. Castellares, Fredy & Patrício, Silvio C. & Lemonte, Artur J., 2020. "On gamma-Gompertz life expectancy," Statistics & Probability Letters, Elsevier, vol. 165(C).
   17. Zelda B. Zabinsky & Pattamon Dulyakupt & Shabnam Zangeneh-Khamooshi & Cao Xiao & Pengbo Zhang & Seksan Kiatsupaibul & Joseph A. Heim, 2020. "Optimal collection of medical specimens and delivery to central laboratory," Annals of Operations Research, Springer, vol. 287(1), pages 537-564, April.
   18. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.
   19. Ho-Hsiang Wu & Marco A. R. Ferreira & Mohamed Elkhouly & Tieming Ji, 2020. "Hyper Nonlocal Priors for Variable Selection in Generalized Linear Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 147-185, February.
   20. Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
   21. Imbert, Clément & Papp, John, 2020. "Costs and benefits of rural-urban migration: Evidence from India," Journal of Development Economics, Elsevier, vol. 146(C).
       • Imbert, Clement & Papp, John, 2020. "Costs and Benefits of Rural-Urban Migration : Evidence from India," The Warwick Economics Research Paper Series (TWERPS) 1253, University of Warwick, Department of Economics.
       • Imbert, Clément & Papp, John, 2020. "Costs and Benefits of Rural-Urban Migration : Evidence from India," CAGE Online Working Paper Series 459, Competitive Advantage in the Global Economy (CAGE).
   22. David Ardia & Keven Bluteau, 2024. "Optimal Text-Based Time-Series Indices," Papers 2405.10449, arXiv.org.
   23. Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
   24. Castellares, Fredy & Patrício, Silvio C. & Lemonte, Artur J. & Queiroz, Bernardo L., 2020. "On closed-form expressions to Gompertz–Makeham life expectancy," Theoretical Population Biology, Elsevier, vol. 134(C), pages 53-60.
   25. Wojciech Białaszek & Przemysław Marcowski & David J Cox, 2020. "Comparison of multiplicative and additive hyperbolic and hyperboloid discounting models in delayed lotteries involving gains and losses," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
   26. Thomas Welchowski & Matthias Schmid, 2019. "Sparse kernel deep stacking networks," Computational Statistics, Springer, vol. 34(3), pages 993-1014, September.
   27. Bergeaud, Antonin & Raimbault, Juste, 2020. "An empirical analysis of the spatial variability of fuel prices in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 131-143.
       • Antonin Bergeaud & Juste Raimbault, 2020. "An empirical analysis of the spatial variability of fuel prices in the United States," Post-Print hal-02370106, HAL.
   28. Schwamborn, R. & Mildenberger, T.K. & Taylor, M.H., 2019. "Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN," Ecological Modelling, Elsevier, vol. 393(C), pages 37-51.
   29. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
   30. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
   31. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
   32. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
   33. Maarten J. Punt & Brooks A. Kaiser, 2021. "Seismic Shifts from Regulations: Spatial Trade-offs in Marine Mammals and the Value of Information from Hydrocarbon Seismic Surveying," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(3), pages 553-585, November.
       • Maarten J. Punt & Brooks A. Kaiser, 2019. "Seismic shifts from regulations: Spatial trade-offs in marine mammals and the value of information from hydrocarbon seismic surveying," Working Papers 2/19, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.
   34. Fuad Dedic & Nina Bijedic & Drazena Gaspar, 2020. "Genetic algorithms as a tool for development of balanced curriculum," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(2B), pages 175-193.
   35. Jose M. Calabuig & Luis M. García-Raffi & Albert García-Valiente & Enrique A. Sánchez-Pérez, 2020. "Evolution Model for Epidemic Diseases Based on the Kaplan-Meier Curve Determination," Mathematics, MDPI, vol. 8(8), pages 1-24, August.
   36. Nikolas Schiozer & Gilberto Tadeu Lima & Michel Alexandre, 2024. "Heterogeneity in pricing behavior in hybrid DSGE-ABM macrodynamics," Working Papers, Department of Economics 2024_26, University of São Paulo (FEA-USP).
   37. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
   38. Heuchenne, Cédric & Jacquemain, Alexandre, 2022. "Inference for monotone single-index conditional means: A Lorenz regression approach," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
   39. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
   40. Chaves, Luis Fernando & Friberg, Mariel D. & Hurtado, Lisbeth A. & Marín Rodríguez, Rodrigo & O'Sullivan, David & Bergmann, Luke R., 2022. "Trade, uneven development and people in motion: Used territories and the initial spread of COVID-19 in Mesoamerica and the Caribbean," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
   41. Wanke, Peter & Araujo, Claudia & Tan, Yong & Antunes, Jorge & Pimenta, Roberto, 2023. "Efficiency in university hospitals: A genetic optimized semi-parametric production function," Operations Research Perspectives, Elsevier, vol. 10(C).
   42. Branislav Panić & Jernej Klemenc & Marko Nagode, 2020. "Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation," Mathematics, MDPI, vol. 8(7), pages 1-30, July.
   43. Mareike Ließ & Ali Sakhaee, 2024. "Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape," Agriculture, MDPI, vol. 14(8), pages 1-20, July.
   44. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
   45. Sun, Mucun & Feng, Cong & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization," Applied Energy, Elsevier, vol. 238(C), pages 1497-1505.
   46. Giovanni Forchini & Raoul Theler, 2023. "Semi-parametric modelling of inefficiencies in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 59(2), pages 135-152, April.
   47. Granado, Igor & Hernando, Leticia & Uriondo, Zigor & Fernandes-Salvador, Jose A., 2024. "A fishing route optimization decision support system: The case of the tuna purse seiner," European Journal of Operational Research, Elsevier, vol. 312(2), pages 718-732.
   48. Durrani, Umair & Lee, Chris, 2024. "A new car-following model with incorporation of Markkula's framework of sensorimotor control in sustained motion tasks," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
   49. Nandan, Rohit & Bandaru, Varaprasad & Meduri, Pridhvi & Jones, Curtis & Lollato, Romulo, 2024. "Evaluating the utility of weather generators in crop simulation models for in-season yield forecasting," Agricultural Systems, Elsevier, vol. 220(C).
   50. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.

10. Katherine Morris & Paul McNicholas & Luca Scrucca, 2013. "Dimension reduction for model-based clustering via mixtures of multivariate $$t$$ t -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 321-338, September.

    Cited by:

    1. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    2. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2014. "Mixtures of skew-t factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 326-335.
    3. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 493-518, September.
    4. Sanjeena Subedi & Paul McNicholas, 2014. "Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 167-193, June.
    5. Nicola Loperfido, 2019. "Finite mixtures, projection pursuit and tensor rank: a triangulation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 145-173, March.
    6. Cristina Tortora & Paul D. McNicholas & Ryan P. Browne, 2016. "A mixture of generalized hyperbolic factor analyzers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 423-440, December.
    7. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.
    8. Vrbik, Irene & McNicholas, Paul D., 2014. "Parsimonious skew mixture models for model-based clustering and classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 196-210.
    9. Reyhaneh Rikhtehgaran & Iraj Kazemi, 2016. "The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 541-562, December.
    10. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.

11. Cecilia Chirieleison & Alessandro Montrone & Luca Scrucca, 2013. "Measuring the Impact of a Profit-Oriented Event on Tourism: The Eurochocolate Festival in Perugia, Italy," Tourism Economics, , vol. 19(6), pages 1411-1428, December.

    Cited by:

    1. Yeongbae Choe & Hany Kim & Hyo-Jae Joun, 2019. "Differences in Tourist Behaviors across the Seasons: The Case of Northern Indiana," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    2. Sérgio J Teixeira & João J Ferreira & Peter Wanke & Jorge Junio Moreira Antunes, 2021. "Evaluation model of competitive and innovative tourism practices based on information entropy and alternative criteria weight," Tourism Economics, , vol. 27(1), pages 23-44, February.
    3. Víctor Lafuente Sánchez & María Devesa Fernández & José à ngel Sanz Lara, 2017. "Economic impact of a religious and tourist event," Tourism Economics, , vol. 23(6), pages 1255-1274, September.
    4. Jordi Suriñach & Josep A. Casanovas & Marién André & Joaquim Murillo & Javier Romaní, 2017. "How to quantify and characterize day trippers at the local level," Tourism Economics, , vol. 23(2), pages 360-386, March.

12. Scrucca, Luca, 2011. "Model-based SIR for dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3010-3026, November.

    Cited by:

    1. Szretter Noste, María Eugenia, 2019. "Using DAGs to identify the sufficient dimension reduction in the Principal Fitted Components model," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 317-320.
    2. Hino, Hideitsu & Wakayama, Keigo & Murata, Noboru, 2013. "Entropy-based sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 105-114.
    3. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    4. Milad Mokhtaridoost & Jordan J. Chalmers & Marzieh Soleimanpoor & Brandon J. McMurray & Daniella F. Lato & Son C. Nguyen & Viktoria Musienko & Joshua O. Nash & Sergio Espeso-Gil & Sameen Ahmed & Kate , 2024. "Inter-chromosomal contacts demarcate genome topology along a spatial gradient," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Coudret, R. & Girard, S. & Saracco, J., 2014. "A new sliced inverse regression method for multivariate response," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 285-299.

13. Scrucca, Luca, 2007. "Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 438-451, September.

    Cited by:

    1. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.
    2. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
    3. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    4. Douzal-Chouakria, Ahlame & Diallo, Alpha & Giroud, Françoise, 2009. "Adaptive clustering for time series: Application for identifying cell cycle expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1414-1426, February.
    5. Allison, David B. & Visscher, Peter M. & Rosa, Guilherme J.M. & Amos, Christopher I., 2009. "Statistical genetics & statistical genomics: Where biology, epistemology, statistics, and computation collide," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1531-1534, March.
    6. Coudret, R. & Girard, S. & Saracco, J., 2014. "A new sliced inverse regression method for multivariate response," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 285-299.
    7. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    8. Marie Chavent & Stéphane Girard & Vanessa Kuentz-Simonet & Benoit Liquet & Thi Nguyen & Jérôme Saracco, 2014. "A sliced inverse regression approach for data stream," Computational Statistics, Springer, vol. 29(5), pages 1129-1152, October.

14. Scrucca, Luca, 2001. "Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 6(i07).

    Cited by:

    1. Raúl de la Fuente-Fernández, 2006. "Impact of Neuroprotection on Incidence of Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-5, December.

Chapters

1. Cecilia Chirieleison & Luca Scrucca, 2016. "CSR Education in Italy: The Case of the University of Perugia," CSR, Sustainability, Ethics & Governance, in: Duygu Turker & Ceren Altuntas & Samuel O. Idowu (ed.), Social Responsibility Education Across Europe, pages 139-159, Springer.

   Cited by:

   1. Andrea Venturelli & Roberta Fasiello & Simone Pizzi, 2021. "CSR Education in Economia Aziendale Curricula: An Overview," Administrative Sciences, MDPI, vol. 11(4), pages 1-11, November.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Luca Scrucca should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.