Director of the Florence Center for Data Science (FDS) and coordinator of the Statistics track for the PhD program in Mathematics, Computer Science, Statistics

Research

 

My primary research interests revolve around the fascinating world of multivariate statistical models, with a particular focus on 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, models for circular data, and in their connection with graphical models. I recently got involved in statistical learning, 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

 

Vannucci G. & Gottard A. (in early view). An evolutionary estimation procedure for Generalized Semilinear Regression Trees. Computational Statistics, 1-23

Gottard A., Vannucci G., Grilli L., Rampichini C. (2023). Mixed-effect models with trees. Advances in Data Analysis and Classification, 17, 431-461

Agresti A., Giordano S. & Gottard A. (2022). A Review of Score-Test-Based Inference for Categorical Data. Journal of Quantitative Economics, vol. 20, pp. 31-48

Gottard A., Vannucci G. & MarchettiG.M. (2020) A note on the interpretation of tree-based regression models Biometrical Journal, 62(6), 1564-1573

Colombi R., Giordano S., Gottard A. (2019) Discussion of “The class of CUB models: statistical foundations, inferential issues and empirical evidence”, Statistical Methods & Applications, 28(3), 441-444.

Colombi R., Giordano S., Gottard A., Iannario M. (2019) Hierarchical Marginal Models with Latent Uncertainty Component, Scandinavian Journal of Statistics, 46, 595-620.

Bravi R., Cohen E.J., Martinelli, Gottard A. & Minciacchi D. (2018) The Less You are, the More You are Helped: Effect of Kinesio Tapeon Temporal Coordination, Int J Sports Med, doi: 10.1055/a-0668-0041.

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 (more or less)

Focardi Olmi L., Gottard A. & Vannucci M, Bayesian Controlled FDR Variable Selection via Knockoffs (submitted)

Gottard A. & Panzera A. Graphical models for circular variables

recent presentations

Anna Gottard (2023). Uncertainty & Fairness metrics. In: Book of Short Papers SIS 2023

 

Teaching

 

MASL: Multivariate Analysis & Statistical Learning - Moodle link

FSL: Fundation of Statistical Learning - Moodle link

 

 
Department of Statistics