Luca Scrucca

Associate Professor of Statistics
Università degli Studi di Perugia

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Academic Bio

Hello, I’m an applied statistician. In a broader sense, I’m also a data scientist because I do data analysis, and I do research on the methodology and computational aspects of data analysis. I’m an enthusiastic R user and package developer.

I’m also serving as Associate Editor for Journal of Statistical Software and Statistics and Computing.

Interests

  • Mixture models
  • Model-based clustering and classification
  • Data Science
  • Statistical learning
  • Dimension reduction methods and regression graphics
  • Genetic and evolutionary algorithms

Education

PhD in Statistics
Università degli Studi di Perugia
2000

MSc Statistics
University of Minnesota
2000

Research

Book

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 Press.

ISBN: 978-1032234953
eBook ISBN: 978-1003277965

Online book

Selected publications

  Scrucca L. (2024) Entropy-based volatility analysis of financial log-returns using Gaussian mixture models. Entropy, 26(11), 907. https://doi.org/10.3390/e26110907

  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

  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

  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

  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

  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

  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

  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. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 5:4, 1–37. http://www.jstatsoft.org/v53/i04

  Full list of publications

Software

I’m the author and/or maintainer of several packages and functions written in R, a free software environment for statistical computing and graphics, using RStudio, an integrated development environment (IDE) for R.

    A collection of R packages for statistical modeling using Gaussian mixtures.

    List of R packages and functions.

Teaching

  Statistica
      Dipartimento di Economia (sede di Terni)
      Corso di Laurea Triennale in “Economia Aziendale”

  Mathematics II and Statistics
      Dipartimento di Ingegneria
      Corso di Laurea Triennale/BSc in “Engineering Management”

  Statistical Learning for Data Science
      Dipartimento di Economia
      Corso di Laurea Magistrale in “Finanza e Metodi Quantitativi per l’Economia”
      Statistical Methods for Data Science
      Dipartimento di Matematica e Informatica
      Corso di Laurea Magistrale in “Informatica”
      Curriculum Artificial Intelligence

  Gaussian Mixture Models for Model Based Clustering, Classification, and Density Estimation
      Dottorato in Matematica Informatica e Statistica (UniFI, UniPG, INdAM)
      Dottorato in Economia (UniPG)


Contact

  Prof. Luca Scrucca
      Dipartimento di Economia
      Università degli Studi di Perugia
   Via A. Pascoli 20, 06123 Perugia (Italy)
   +39-075-5855231
   +39-075-5855950