Research

Research Interests

•     Evolutionary computation in statistical applications

•     Computational statistics

•     Statistics for drug discovery and development

•     Causal inference

•     Time series analysis

Research Branch

• Statistics (SECS-S/01 in the Italian system)

Publications

Journal papers

• Grünewald, S., Stecklum, M., Rizzo, M., Rathjens, J., Fiebig, L., Zopf, D. (2023) Effects of regorafenib on the mononuclear/phagocyte system and how these contribute to the inhibition of colorectal tumors in mice. European Journal of Medical Research 28(147) (link)

• Battaglia, F., Cucina, D., Rizzo, M. (2020) Detection and estimation of additive outliers in seasonal time series. Computational Statistics. 35(3), 1393-1409 (link)

• Battaglia, F., Cucina, D., Rizzo, M. (2020) Parsimonious periodic autoregressive models for time series with evolving trend and seasonality. Statistics and Computing. 30(1), 77-91 (link)

• Cucina, D., Rizzo, M., Ursu, E. (2019) Multiple changepoint detection for periodic autoregressive models with an application to river flow analysis. Stochastic Environmental Research & Risk Assessment., 33(4), 1137-1157 (link)

• Rizzo, M., Battaglia, F. (2018) Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation. Journal of Statistical Computation and Simulation, 88(16), 3081-3097. (link)

• Rizzo, M., Battaglia, F. (2016) On the Choice of a Genetic Algorithm for Estimating GARCH Models. Computational Economics, 48(3), 473-485 (link)

Conference papers and Book chapters

• Friedrich, T., Krejca, M.S., Lagodzinski, J.A.G., Rizzo, M., Zahn, A. (2020) Memetic Genetic Algorithms for Time Series Compression by Piecewise Linear Approximation. In H. Yang, K. Pasupa, A.CS. Leung, J.T. Kwok, J.H. Chan, I. King (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science, vol 12534. Springer, Cham.  (pp 592-604). ISBN: 978-3-030-63836-8 (link) (link) (book link) (repository)

• Cucina, D., Rizzo, M., Ursu, E. (2018) Identification of multiregime periodic autoregressive models by genetic algorithms. In O. Valenzuela, F. Rojas, H. Pomares, I. Rojas (eds) ITISE 2018 - International Conference on Time Series and Forecasting - Proceedings of Papers Vol. 1. Godel Impresiones Digitales (pp. 396-407). ISBN: 978-84-17293-57-4 (book link)

• Battaglia, F., Cucina, D., Rizzo, M. (2018) Generalized periodic autoregressive models for trend and seasonality varying time series. In A. Abbruzzo, E. Brentari, M. Chiodi, D. Piacentino (eds) Book of Short Papers SIS 2018. Pearson. ISBN: 9788891910233 (link) (book link)

• Battaglia, F., Cucina, D., Rizzo, M. (2018) Periodic autoregressive models with multiple structural changes by genetic algorithms. In M. Corazza, M. Durbàn, A. Granè, C. Perna, M. Sibillo (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance - MAF 2018. Springer (pp. 107-110). ISBN: 978-3-319-89824-7 (link) (book link)

• Rizzo, M. (2017) On Variability Analysis of Evolutionary Algorithm-Based Estimation. In F. Greselin, F. Mola, M.A. Zenga (eds) Cladag 2017 Book of Short Papers. Universitas Studiorum. ISBN: 978-88-99459-71-0 (link) (book link)

Miscellanea

• Battaglia, F., Cucina, D., Rizzo, M. (2018) A generalization of periodic autoregressive models for seasonal time series. Technical Report N.2. Department of Statistical Sciences, Sapienza University of Rome. ISSN: 2279-798X (link)

• Rizzo, M. (2018) Contributions on Evolutionary Computation for Statistical Inference. Ph.D Thesis. Sapienza University of Rome (link)