Location The organisers are investigating options for an online meeting

Schedule and abstracts:
1:30 - 2pm: Welcome

2pm to 2:50pm 

Thomas Mikosch: Extreme value theory for the entries of a sample covariance matrix
This is joint work with Johannes Heiny (Bochum) and Jorge Yslas (Copenhagen). We derive the point process convergence of the off-diagonal entries of a large sample covariance matrix based on iid data toward a Poisson process. We show how the dimension $p=p_ntoinfty$ as $ntoinfty$ and the tail behaviour of the entries are linked. These entries constitute dependent random walks and we are interested in their joint tail behavior. Our main tools for proving these results are precise large deviation results for sums of independent random vectors.

2:50pm to 3:40pm

Alexey Onatskiy: Spurious factor analysis

This talk draws parallels between the Principal Components Analysis of factorless high-dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is collaborated by the standard panel information criteria. Furthermore, the Dickey-Fuller tests of the unit root hypothesis applied to the estimated "idiosyncratic terms" often reject, creating an impression that a few factors are responsible for most of the non-stationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.

3:40pm to 4:10pm: Coffee Break

4:10pm to 5pm
Sandrine Peche: Non-linear random matrix ensembles

We consider a random matrix model M = YY? where Y = (f(WX)_ij) and W and X are large rectangular matrices with iid entries.The function f is called the activation function in certain neural networks. Pennington and Worah have identified the empirical eigenvalue distribution of such random matrices in the Gaussian case (W and X). We extend their result to a wider class of distributions for a certain class of activation functions. This is joint work with Lucas Benigni.