Marco Geraci Curriculum

Professor Geraci obtained a Laurea (MSc) magna cum laude in Economics from the University of Sassari (Italy) in 2000 and a PhD in Applied Statistics from the University of Florence (Italy) in 2005. He carried out academic research in several institutions, including the National Council of Research (Italy), the University of Manchester (UK), University College London (UK) and the University of South Carolina (USA), where he currently holds an appointment as Adjunct Professor of Biostatistics. His research interests are in statistical methods and applications for health sciences, quantile inference, random-effects models, multivariate statistics, missing data, statistical computing, programming (R and C/C++), spatial statistics, accelerometer data, epidemiology and pediatrics.

Professor Geraci is involved in a wide range of collaborations in methodological and applied research. He has published peer-reviewed articles in statistics, cancer epidemiology, maternal and child health epidemiology, physical activity (accelerometry data), gastroenterology, nuclear medicine and higher education. He also authored four statistical R packages on CRAN. He received funding awards for both methodological and collaborative research projects including a major Center grant (NIH P20) for 11 million dollars as lead of the Statistical and Data Management Core, several methodological grants (e.g., NIH R03 and intramural funding) as principal investigator for thousands of dollars, several collaborative grants (NIH R01 and R03) totalling 10 million dollars as co-investigator and lead statistician.

In 2010, Professor Geraci was awarded Chartered Statistician by the Royal Statistical Society. He obtained the National Scientific Qualification (Abilitazione Scientifica Nazionale) as Full Professor of Statistics in 2017 (settore concorsuale 13/D1) and as Full Professor of Medical Statistics in 2019 (settore concorsuale 06/M1). He was Statistical Editor for the Journal of Child Health Care (SAGE) and Associate Editor for the Journal of Applied Statistics (Taylor & Francis). He is currently Associate Editor for Statistical Methods and Applications (Springer) and Board Member of Significance (Wiley on behalf of the Royal Statistical Society – RSS and the American Statistical Association – ASA). He is an RSS fellow since 2006 and ASA member since 2015. He performed more than 200 reviews for 45 distinct journals (verified on Publons) including the Journal of the American Statistical Association, Journal of the Royal Statistical Society A, Journal of Statistical Planning and Inference, American Sociological Review, Annals of Applied Statistics, Journal of Statistical Software, Scandinavian Journal of Statistics, Statistical Methods in Medical Research, Statistics and Computing, Statistics in Medicine, as well as journals from the Lancet group. He has been awarded Publons Top 1% Reviewers for multidisciplinary (2017) and cross-field (2019).

Selected publications:

  • Geraci M and Farcomeni A (2020). A family of linear mixed-effects models using the generalized Laplace distribution. Statistical Methods in Medical Research, 29, 2665-2682.
  • Geraci M (2019). Modelling and estimation of nonlinear quantile regression with clustered data. Computational Statistics & Data Analysis, 136, 30-46.
  • Geraci M (2018). Additive quantile regression for clustered data with an application to children’s physical activity. Journal of the Royal Statistical Society C, 68, 1071-1089.
  • Geraci M and McLain A (2018). Multiple imputation for bounded variables. Psychometrika, 83, 919-940.
  • Geraci M (2016). Estimation of regression quantiles in complex surveys with data missing at random: An application to birthweight determinants. Statistical Methods in Medical Research, 25, 1393-1421.
  • Geraci M and Farcomeni A (2016). Probabilistic principal component analysis to identify profiles of physical activity behaviours in the presence of nonignorable missing data. Journal of the Royal Statistical Society C, 65, 51-75.
  • Geraci M (2014). Linear quantile mixed models: The lqmm package for Laplace quantile regression. Journal of Statistical Software, 57, 1-29.
  • Geraci M and Bottai M (2014). Linear quantile mixed models. Statistics and Computing, 24, 461-479.
  • Geraci M and Bottai M (2007). Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics, 8, 140-154.
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