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STAD


STAD research group aims at spreading the successful adoption of statistical approach and data analysis in various fields of research so to foster the value chain that leads from raw data to knowledge, according to a well-known scientific paradigm based on the statistical learning.
STAD embodies professors and researchers enrolled by different departments at UniNA (DII, DSES, DSP). Moreover is a hub connecting statisticians who work in the academia as well as in public and private sectors in Italy and abroad allowing the integration of different methodological research activities as well applications.
STAD vision is "Make it better with Statistics, Technology, Analysis of Data!"

The research areas and domain expertise of STAD are:

  • Generalized Linear Models
  • Categorical Data Analysis
  • Preference rankings
  • Supervised and Unsupervised Classification
  • Recursive partitioning
  • Linear and non linear regression
  • Maximum Likelihood Estimation
  • Multivariate Data Analysis
STAD senior members have been responsible of many scientific projects funded by EU and national institutions. Recently they has been involved into a huge H2020 project about informing NEXUS security (i.e. sustain policy makers for the governance of water-energy-food and land use nexus by sound statistics) called MAGIC: www.magic-nexus.eu. Roberta Siciliano (mentore), Valerio Tutore, Massimo Aria, Antonio D'Ambrosio, whose first letters of their surnames form, amazingly, another STAD!

MAGIC H2020 project

H2020 project

STAD team has been recently granted with an EU H2020 project about informing NEXUS security, i.e. sustain policy makers for the governance of water-energy-food and land use nexus by sound statistics.

METHODOLOGY

Browse our research areas and see the last publications.

CASE STUDIES

A lot of applications in many different fields: statistics is useful everywhere.    

FRIENDS' NETWORK



A strong network of friends is at the heart of STAD.

STAD

Statistics, Technology, Analysis of Data
Mantainer: Michele Staiano 2016