Statistical Learning Seminars

The effects of CoViD-19 pervade through research communities across the globe, causing canceled conferences, postponed research visits, and suspended projects. Like many others, we have sought other opportunities for collaboration in spite of the current state of affairs and have therefore organized this online seminar series in statistical learning.

Format

We use zoom for all the sessions. Upon joining the seminar, you will be placed in a waiting room; please wait for the host to let you in to the meeting.

The seminars are approximately an hour long with anywhere between 20 and 40 minutes allocated to the presentation and the rest for discussion. Sessions are held on a regular basis on Fridays at 15:30 CET. See Previous Talks for recordings, slides, and resources from previous seminars.

https://lu-se.zoom.us/j/65067339175

Mailing List

To receive announcements for upcoming seminars, please join the group at https://groups.google.com/g/statlearnsem.

Calendar Event

Link to calendar event

Upcoming Talks

Friday, October 7 15:30 CET

Sandra Paterlini (University of Trento)

Title
Sparse Graphical Modelling for Financial Applications
Abstract
Graphical models have shown remarkable performance in uncovering the conditional dependence structure across a set of given variables. In this paper, we introduce two new graphical modelling approaches—called Gslope and Tslope—to the portfolio selection literature for directly estimating the inverse of the covariance matrix, using the so called Sorted L1-Penalized Estimator (SLOPE). Spanning Gaussian and non- Gaussian distributed data, our new methods directly acknowledge the empirically observed distributional characteristics of asset returns. Extensive simulation analysis and real-world applications highlight the superiority of our new methods, especially with regard to clustering and stability characteristics, compared to state-of-the-art covariance matrix estimation techniques.

Related Work
Sparse Graphical Modeling for Minimum Variance Portfolios
Sparse Graphical Modelling via the Sorted L1-Norm
About
Sandra Paterlini is a full professor at the Department of Economics and Management at the Univeristy of Trento, Italy.

Organization

This seminar series is a joint effort organized by The Department of Mathematics, Wrocław University, The Department of Mathematics, University of Burgundy, and The Department of Statistics, Lund University.

Lund University
University of Burgundy
Wroclaw University