My research interests revolve mainly around multivariate statistical models, and, in particular, to graphical models. A graphical model is a multivariate statistical model that can be associated to a graph: nodes correspond to random variables and missing edges to  conditional independences. I am also interested in marked point processes for event history analysis, multilevel and mixture models in non-standard situations, and in their connection with graphical models. I recently got involved in metabolomics, data science and models for high-dimensional data. As I love to be challenged by new topics, I have absolutely no idea on what I'm going to do in my future.

Some publications


Kateri M., Gottard A. & Tarantola C. (2017) Generalized Quasi Symmetry Models for Ordinal Contingency Tables,  Australian & New Zealand Journal of Statistics, 59(3), 239-253.

Bravi R., Cohen E.J., Martinelli A., Gottard A. & Minciacchi D. (2017) When Non-Dominant Is Better than Dominant: Kinesiotape Modulates Asymmetries in Timed Performance during a Synchronization-Continuation Task, Frontiers in Integrative Neuroscience, 11.

Gottard A. & Calzolari G. (2017) Alternative estimating procedures for multiple membership logit models with mixed effects: indirect inference and data cloning, Journal of Statistical Computation and Simulation, 87(12), 2334-2348.

Gottard, A., Iannario, M. & Piccolo D. (2016) Varying uncertainty in CUB models, Advances in Data Analysis and Classification, 10(2), 225-244.

Mencarini, L., Vignoli, D. & Gottard A. (2015) Fertility intentions and outcomes. Implementing the Theory of Planned Behavior with graphical models, Advances in Life Course Research, 23, 14-28.

Gottard A., Mattei, A. & Vignoli, D. (2015) The relationship between education and fertility in the presence of a time-varying frailty component, Journal of the Royal Statistical Sociaty A, 178(4), 787-1114.

Bravi R., Quarta E., Cohen E.J., Gottard A. & Minciacchi D. (2014) A little elastic for a better performance: kinesiotaping of the motor effector modulates neural mechanisms for rhythmic movements, Frontiers in Systems Neuroscience, 8.

Gottard A., Stanghellini E. & Capobianco R. (2013) Semicontinuous regression models with Skew distributions. In Complex Models and Computational Methods in Statistics, M. Grigoletto, F. Lisi, S. Petrone Eds. Springer, 149 - 160.

Gottard A., Marchetti G.M. & Agresti A. (2011) Quasi-symmetric graphical log-linear models. Scandinavian Journal of Statistics, 38, 447-465.

Gottard A. & Pacillo S. (2010) Robust concentration graph model selection, Computational Statistics and Data Analysis, 54, 12, 3070 - 79.

Catalani C., Gottard A., Benvenuti M., Frati E., Rossi A., Giuffreda G. & Baldi L. (2008) Prevalence of HBV, HDV, HCV invection and alleged risk factors in the Pistoia (Italy) haemodialysis population, Italian Journal of Allergy and Clinical Immunology, 18, 22 - 29.

Gottard A. (2007) On the inclusion of bivariate marked point processes in graphical models, Metrika, 66, 269 - 287.

Gottard A. & Pacillo S. (2007) On the impact of contaminations in graphical Gaussian models, Statistical Methods & Applications, 15, 343 - 354.

Agresti A. & Gottard A. (2007) Nonconservative exact small-sample inference for discrete data, Computational Statistics and Data Analysis, 51, 6447 - 6458.

Gottard A. & Rampichini C. (2007) Chain Graphs for Multilevel Models, Statistics & Probability letters, 77, 312 - 318.

Agresti A. & Gottard A. (2007) Independence in multi-way contingency tables: S. N. Roy's breakthroughs and later developments, Journal of Statistical Planning and Inference, 137, 3216 - 3226.

Dreassi E. & Gottard A. (2007) A Bayesian approach to model interdependent event histories by graphical models, Statistical Methods & Applications, 16, 39 - 49.

Gottard A., Grilli L. & Rampichini C. (2006) A Multilevel Chain Graph Model for the Analysis of Graduates' Employment, in Effectiveness of University Education in Italy: Employability, Competencies, Human Capital,  L. Fabbris, ed. Springer, 169 - 182.

Agresti A. & Gottard A. (2005) Randomized confidence intervals and the mid-P approach, comment on Geyer and Meeden, Statistical Science, 20, 367 - 371.

Catalani C., Biggeri A., Gottard A., Benvenuti M., Frati E. & Cecchini C. (2004) Prevalence of HCV infection among health care workers in a hospital in Central Italy, European Journal of Epidemiology, 19, 73 - 77.

recently submitted

Colombi R., Giordano S., Gottard A., Iannario M. Hierarchical Marginal Models with Latent Uncertainty Component , submitted.

Bravi R., Cohen E.J., Martinelli, Gottard A. & Minciacchi D. Tampering with the control of rhythmic movements: the effect of kinesiotaping on dominant-non dominant asymmetries in timing precision, submitted.

recent proceedings

Colombi R., Giordano S., Gottard A., Iannario M. (2016) Ordinal Responses with Latent Uncertainty: a Bivariate Model, Proceedings of the 31th International Workshop on Statistical Modelling.

Colombi R., Giordano S., Gottard A., Iannario M. (2016) Modelling uncertainty in bivariate models for ordinal responses, SIS2016 Proceedings.

Gottard A. (2013) Some considerations on VCUB models. Cladag 2013. 9th Meeting of the Classiffcation and Data Analysis Group. Book of Abstracts. Eds. T. Minerva, I. Morlini, F. Palumbo, 241 - 244.

Gottard A. (2013) A joint Bradley-Terry model for tennis tournaments via Data Cloning. Proceedings of the 28th International Workshop on Statistical Modelling, Eds. Muggeo V.M.R., Capursi V., Boscaino G., Lovison G., 171 - 176.

Gottard A., Mattei A. & Vignoli D. (2011) Modelling fertility and education in Italy in the presence of time-varying frailty component, Proceedings of 26th International Workshop on Statistical Modelling, Valencia (Spain) July 11 - 15, 2011. Eds. Conesa, D., Forte, A., Lopez-Quilez, A. & Munoz, F., 281-286.

Marchetti G.M., Vannini I., Gottard A. & Vignoli D. (2011) Regression graph models: an application to joint modelling of fertility intentions among childless couples, Proceedings of 26th International Workshop on Statistical Modelling, Valencia (Spain) July 11 - 15, 2011. Eds. Conesa, D., Forte, A., Lopez-Quilez, A. & Munoz, F. Pag. 358-363.



 1st semester


- MASL: Multivariate Analysis & Statistical Learning - Moodle link

This course concerns the analysis of multivariate data from a point of view that connects theoretical principles with practical applications in several frameworks: social science, bioscience, marketing, epidemiology - in English.

- CPS: Calcolo delle probabilita' e statistica - Moodle link

This course introduces the basic concepts of probability and statistics for students in Computer Science and Informatics - in Italian.