Catia Scricciolo

  1. Home
  2. Department
  3. People
  4. Catia Scricciolo
CS, June 2, 2018
Position
Associate Professor
Academic sector
SECS-S/01 - STATISTICS
Research sector (ERC)
PE1_14 - Statistics

Office
Polo Santa Marta,  Floor 1,  Room 1.25
Telephone
045 802 8341
E-mail
catia|scricciolo*univr|it <== Replace | with . and * with @ to have the right email address.

Office Hours

Office hours via video-call, usually on Fridays at 3:00 p.m., by prior arrangement via e-mail.

Curriculum
  • pdf   CV EN   (pdf, en, 338 KB, 16/03/21)
  • pdf   CV ITA   (pdf, it, 345 KB, 16/03/21)

Associate Professor of Statistics at Università di Verona since 2015. Previously she was Assistant Professor in Statistics at Università Bocconi of Milano. She earned a degree in Statistics from Università "La Sapienza" di Roma and received a Ph.D. in Statistics from Università di Padova. Her research activity is focused on Statistical Inference, in particular on Bayesian Nonparametric Inference with reference to the following aspects:
 
  • nonparametric curve estimation,
  • mixture models,
  • empirical Bayes procedures,
  • Bayesian inverse problems.

Modules

Modules running in the period selected: 19.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
PhD in Economics and Management (click to insert) Statistica (2020/2021)   7.5    7.5 
Bachelor's degree in Business Administration (Verona) Statistics (2020/2021)   9  eLearning 9 
Master’s degree in Management and business strategy Statistics for Business (2020/2021)   6  eLearning 0.5 
Master’s degree in Banking and Finance Stochastic Models for Finance (2020/2021)   9  eLearning 1 
Bachelor's degree in Business Administration (Verona) Statistics (2019/2020)   9  eLearning 9 
Master’s degree in Banking and Finance Stochastic Models for Finance (2019/2020)   9  eLearning 1 
PhD in Economics and Management Attività didattica dottorato (2019/2020)   50    1 
Bachelor's degree in Business Administration (Verona) Statistics (2018/2019)   9  eLearning 9 
Master’s degree in Banking and Finance Stochastic Models for Finance (2018/2019)   9  eLearning 1 
PhD in Economics and Management (click to insert) Statistica (2017/2018)   7.5    1 
Bachelor's degree in Business Administration (Verona) Statistics (2017/2018)   9  eLearning 9 
Master’s degree in Banking and Finance Stochastic Models for Finance (2017/2018)   9  eLearning 1 
PhD in Economics and Management Attività didattica dottorato (2017/2018)   10    1 
Bachelor's degree in Business Administration (Verona) Statistics (2016/2017)   9  eLearning 3  (esercitazione)
6  (lezione)
Master’s degree in Banking and Finance Stochastic Models for Finance (2016/2017)   9  eLearning 1 
Bachelor's degree in Business Administration (Verona) Statistics (2015/2016)   9  eLearning 6  (lezione)
3  (esercitazione)
Master’s degree in Banking and Finance Stochastic Models for Finance (2015/2016)   9  eLearning 1 

Advanced teaching activities
Name Online
Activities PhD Course In Economics and Management (35° ciclo - PhD in Economics and Management)
 
Research interests
Topic Description Research area
MSC 62F15 - Bayesian inference Bayesian theory of function estimation in nonparametric statistical models, including the study of credible sets to provide a data-driven quantification of the uncertainty for point estimators. Analysis also covers inverse problems, such as deconvolution, wherein the object of interest has to be recovered from indirect noisy observations. Quantitative Methods for Economics
Parametric inference
MSC 62G20 - Asymptotic properties Analysis of likelihood-based procedures: - consistency and rates of convergence of nonparametric maximum likelihood estimators in Hellinger distance; - theory of frequentist asymptotic properties for nonparametric Bayes procedures, including general contraction rate results for posterior distributions, adaptive estimation and coverage properties of nonparametric credible sets. Quantitative Methods for Economics
Nonparametric inference