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
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.
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.
recently submitted (more or less)
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