Advanced Statistics
People
Description
This course builds on knowledge acquired in the Statistics course at the Master in Finance.
The following topics will be critically covered both from a frequentist and from a Bayesian prospective highlighting the pros and cons of the two approaches: advanced point estimation, confidence intervals, hypothesis testing and prediction. We will also introduce Monte Carlo simulations techniques such as Markov chain Monte Carlo and Approximate Bayesian Computation.
Additional advanced topics will be discussed such as dimensionality reduction and variable selection.
Suggested text books:
For the frequentist approach: Foundations of Statistics for Data Scientists: With R and Python. A. Agresti and M. Kateri, 2021
For the Bayesian approach: Bayesian Data Analysis, Gelman, A. and Carlin, J. B. and Stern, K. S. and Dunson, D. B. and Vehtari, A. and Rubin, D. B., 2014
For the applied part of the course students are referred to the online material available here https://www.r-project.org
Lecture notes will also be provided during the course.
Students are requested to bring to class their own laptop, if available.
Objectives
The course aims to deepen notions of frequentist inferencial statistics. The Bayesian approach to statistical inference will also be covered from a theoretical and applied point of view. The students will be able to critically analyze a given data set having in mind research questions and hypothesis to be tested. The freeware statistical software R Project will be used for the more applied and data driven part of the course.
Teaching mode
In presence
Learning methods
There will be theoretical and applied (with the R statistical software) frontal lectures
Examination information
Class participation is a mandatory component of the course grade.
There will be a final exam that will comprise 100% of the final grade.
The exam will consist on a project comprising a statistical data analysis performed using the inferential tools introduced in the course. The students have to turn in the R or Python code, a PPT presentation and the final report (pdf) where the data are described together with the research questions, the analysis, the conclusions and possible further research directions. The project will be presented and discussed with possible questions ranging on the topics covered in class.
Bibliography
Education
- Master of Science in Economics in Finance, Lecture, Corsi a scelta o Field Project o Semestre all'estero (Digital Finance), Elective, 2nd year
- Master of Science in Economics in Finance, Lecture, Corsi a scelta o Field Project o Semestre all'estero (Banking and Finance), Elective, 2nd year
- Master of Science in Economics in Finance, Lecture, Corsi a scelta o Field Project o Semestre all'estero (Quantitative Finance), Elective, 2nd year
Prerequisite
- Statistics, Mira A., Ghilotti L., Peluso S., SA 2021-2022