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Rubrica

Personale Strutture

Qualifica

Professore Associato

Indirizzo

VIA GIUSTINIANI, 2 - PADOVA

Telefono

0498274173

- Bachelor's Degree in Statistical and Economic Sciences obtained from the Faculty of
Statistical, Demographic, and Actuarial Sciences at the University of Padua on July 17,
1992, with a score of 108/110.
- PhD in Statistics, obtained on October 29, 1996, with a thesis entitled "Statistical In-
ference based on Empirical Likelihood Function: Some Developments".
- Post-Doctoral Research fellow in the disciplinary area of Economic and Statistical Scien-
ces, at the University of Padua, March 1997.

Currently, he is Associate Professor in Statistics at the Department of Cardiac, Thoracic,
Vascular Sciences and Public Health of the University of Padua. He was a Researcher in
Statistics (scientific-disciplinary sector SECS-S/01 in Italy) at the Faculty of Statistical
Sciences of the University of Padua since December 10, 1997. He obtained eligibility
for the role of Associate Professor in the Statistics in September 2001. He assumed the
position of Associate Professor in January 2005 at the Faculty of Statistical Science of the
University of Padua. He obtained the scientific qualification (ASN) for the role of Full
Professor in Statistic in July 2018.

Avvisi

UBEP - Office address: via Loredan 18,
Phone number: 049 8275780.

Pubblicazioni

https://www.research.unipd.it/simple-search?query=gianfranco+adimari

Area di ricerca

The research activity initially focused (and partially still does today) on the study
of pseudo-likelihoods, particularly empirical likelihood and quasi-likelihood, with interest
in methodological and theoretical aspects, applications to inferential problems in non-
parametric settings, also characterized by incomplete data, and applications to robust
inference problems.
In recent years, research activity has also focused on studying statistical techniques
for evaluating diagnostic tests. In particular, some inferential problems related to ROC
analysis have been addressed, concerning Receiver Operating Characteristic (ROC) cur-
ves and surfaces, and derived indicators, even in a context of incomplete information
(verification bias), clustered data or the presence of covariates.