Catia Scricciolo

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CS, May 17, 2021
Position
Associate Professor
Academic sector
STAT-01/A - Statistics
Research sector (ERC-2024)
PE1_14 - Mathematical statistics

PE1_15 - Generic statistical methodology and modelling

PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

Research sector (ERC)
PE1_14 - Statistics

Office
Polo Santa Marta,  Floor 1,  Room 1.25
Telephone
045 8028341
E-mail
catia|scricciolo*univr|it <== Replace | with . and * with @ to have the right email address.

Office Hours

Office hours: Wednesday 12:00--13:00 tbc by prior arrangement via e-mail.

Curriculum
  • pdf   CV EN   (pdf, en, 217 KB, 03/09/23)
  • pdf   CV ITA   (pdf, it, 218 KB, 03/09/23)

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: 26.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Master’s degree in Economics and Data Analysis Machine Learning for Economics (2023/2024)   6  eLearning 4 
Bachelors' degree in Business Administration and Management Statistics (2023/2024)   9  eLearning 9 
Ph.D. in Economics and Finance Mathematical Statistics (2023/2024)   5  eLearning 5 
Master’s degree in Economics and Data Analysis Machine Learning for Economics (2022/2023)   6  eLearning 4 
Bachelors' degree in Business Administration and Management Statistics (2022/2023)   9  eLearning 7 
Master’s degree in Economics and Data Analysis Machine Learning for Economics (2021/2022)   6  eLearning 4 
Bachelors' degree in Business Administration and Management Statistics (2021/2022)   9  eLearning 7 
PhD in Economics and Management 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 
PhD in Economics and Management Lezioni Dottorandi (2019/2020)   50    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 
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 Lezioni Dottorandi (2017/2018)   10    1 
PhD in Economics and Management 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 
Bachelor's degree in Business Administration (Verona) Statistics (2016/2017)   9  eLearning 6  (lezione)
3  (esercitazione)
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    6  (lezione)
3  (esercitazione)
Master’s degree in Banking and Finance Stochastic Models for Finance (2015/2016)   9    1 

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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


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